26 Rag Architecture Jobs

Join our team to industrialize sophisticated cutting-edge algorithms for our next generation of BMW Cars. The role involves designing, developing, and refining generative models that will form the core of our innovative AI-driven products and services. The ideal candidate will possess deep expertise in generative AI practices, with the ability to implement models that can synthesize novel content, ranging from text and images to complex data patterns. In your daily work, you will find yourself in an international and interdisciplinary environment with an agile mindset. Our campus offers the cloud infrastructure you need to work productively with large amounts of data and focus on the software for the automobile of the future. What should you bring along! 5+ years of experience in AI/ML research and development Experience and deepen knowledge about Generative AI Proven experience with generative model architectures such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and autoregressive models like Transformer-based networks, LLMs, SLMs, Knowledge about generative AI techniques and fine-tuning methods to improve model performance. Familiar with implementing RAG (Retrieval-Augmented Generation) architecture for sophisticated AI applications Extensive knowledge of multimodal models, foundation models, and fine-tuning techniques. In-depth understanding of transformer architectures, RAG architecture Basic knowledge of AI accelerator chips Knowledge of MLOps practices to ensure the robustness and reliability of AI systems Expertise in designing and implementing scalable and efficient AI systems Strong Python programming skills and experience with modern AI frameworks Excellent communication and collaboration skills, with the ability to lead and mentor junior team members Knowledge of software development processes in the automotive environment is advantageous Familiarity with cloud computing platforms and services for AI/ML (AWS) Business fluent knowledge of English (written and spoken)

About the Role We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions. You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems. Responsibilities Generative AI Pipeline Development Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads. Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs. Build CI/CD pipelines with integrated prompt regression testing and version control. Use orchestration frameworks like LangChain or LangGraph for tool routing and multi-hop workflows. Monitor system performance using tools like Langfuse or Prometheus. Data and Document Ingestion Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data. Apply preprocessing pipelines for text, images, and code. Ensure data integrity, format consistency, and security across sources. AI Service Integration Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.). Build internal APIs for smooth backend-AI communication. Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets. Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy. Retrieval-Augmented Generation (RAG) Pipelines Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API). Design custom retrieval strategies for multi-modal or multi-source documents. Apply post-retrieval ranking using DPO or feedback-based techniques. Improve contextual relevance through re-ranking, chunk merging, and scoring logic. LLM Integration and Optimization Manage prompt engineering, model interaction, and tuning workflows. Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design. Optimize generation using temperature tuning, token limits, and speculative decoding. Integrate observability and cost-monitoring into LLM workflows. Backend Services Ownership Design and maintain scalable backend services supporting GenAI applications. Implement monitoring, logging, and performance tracing. Build RBAC (Role-Based Access Control) and multi-tenant personalization. Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production. Required Skills and Qualifications Education Bachelors or Masters in Computer Science, Artificial Intelligence, Machine Learning, or related field. Experience 5+ years of experience in AI/ML engineering with end-to-end pipeline development. Hands-on experience building and deploying LLM/RAG systems in production. Strong experience with public cloud platforms (AWS, Azure, or GCP). Technical Skills Proficient in Python and libraries such as Transformers, SentenceTransformers, PyTorch. Deep understanding of GenAI infrastructure, LLM APIs, and toolchains like LangChain/LangGraph. Experience with RESTful API development and version control using Git. Knowledge of vector DBs (Qdrant, FAISS, Weaviate) and similarity-based retrieval. Familiarity with Docker, Kubernetes, and scalable microservice design. Experience with observability tools like Prometheus, Grafana, or Langfuse. Generative AI Specific Skills Knowledge of LLMs, VAEs, Diffusion Models, GANs. Experience building structured + unstructured RAG pipelines. Prompt engineering with safety controls, schema enforcement, and hallucination mitigation. Experience with prompt testing, caching strategies, output filtering, and fallback logic. Familiarity with DPO, RLHF, or other feedback-based fine-tuning methods. Soft Skills Strong analytical, problem-solving, and debugging skills. Excellent collaboration with cross-functional teams: product, QA, and DevOps. Ability to work in fast-paced, agile environments and deliver production-grade solutions. Clear communication and strong documentation practices. Preferred Qualifications Experience with OCR, document parsing, and layout-aware chunking. Hands-on with MLOps and LLMOps tools for Generative AI. Contributions to open-source GenAI or AI infrastructure projects. Knowledge of GenAI governance, ethical deployment, and usage controls. Experience with hallucination suppression frameworks like Guardrails.ai, Rebuff, or Constitutional AI. Shift Time: 2:30 PM to 11:30 PM IST Location-Remote,Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

Job Description: Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. We are seeking an experienced and highly skilled Principal Data Scientist to join our team as an AI Builder. The ideal candidate will have a strong background in data science, machine learning, and AI, with a proven track record of developing and deploying advanced AI models to solve complex healthcare challenges. This role requires leadership, innovation, and the ability to drive AI initiatives from concept to production. Key Responsibilities: Lead and conduct advanced research in AI/ML to drive innovation in healthcare. Design, develop, and implement machine learning models and algorithms to solve complex healthcare problems. Lead the delivery of high-impact analytics solutions, integrating advanced AI and Generative AI components to support business use cases within the Analytics group Design and manage development of modular, reusable, and maintainable software supporting the Quality organization and strategic analytics initiatives Maintain hands-on involvement in solution development, ensuring rapid response to bugs and security vulnerabilities across owned code repositories Apply and promote software engineering best practices, fostering technical excellence across the engineering community Collaborate extensively with teams across Security, Compliance, Engineering, Product Management, Service Management, and Business Operations to ensure alignment and successful execution Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Key Skills: Expert-level Python for production systems. In-depth knowledge of LLM vulnerabilities and implementation of guardrails. Proven experience working with AWS SageMaker for model development, deployment, and monitoring. Deploy applications in orchestrated environments like Kubernetes (AKS) using Docker. Apply explainable AI (AXI) tools such as LIME and SHAP to ensure transparency and interpretability of AI models. Apply deep learning algorithm techniques, open-source tools, and technologies. Implement classical machine learning algorithms such as Logistic Regression, Decision Trees, Clustering (K-means, Hierarchical, and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, and Recommender Systems. Develop deep learning algorithms like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks (Feedforward, CNN, LSTMs, GRUs). Experience with cloud platforms (AWS preferred), including SageMaker. Proficiency in statistical analysis tools and libraries (e.g., NumPy, Pandas, PyMC3, or similar) Hands-on experience with FastAPI and Pydantic for building microservices. Qualifications: Bachelors or equivalent experience in Computer Science, Data Science, AI/ML, or a related field. 8+ years of proven experience in leading AI/ML research projects and teams. Strong programming skills in Python, R, and SQL. Excellent problem-solving skills and the ability to work independently and collaboratively. Strong communication skills and the ability to present complex technical concepts to non-technical stakeholders. Familiarity with AIML governance, ethics, and responsible AI practices Key Skills: Proficiency AutoML: Automated Machine Learning (AutoML) tools like Azure ML Studio, Google Cloud AutoML, and DataRobotStrong statistical and mathematical knowledge - Must have Deep Learning algorithm techniques, open-source tools and technologies, statistical tools, and programming environments such as Python, PySpark and SQL - Must have Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil – Must have Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech or Image processing – Must Have Deep Learning frameworks for Production Systems like TensorFlow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono – Must Have Exposure or experience using code version management tools such as: GitHub - Must have Synthetic Data Generation: Tools like Gretel.ai and Synthea are used to generate synthetic data, which can be useful for training models when real data is scarce or sensitive - Good to Have Generative AI experience - LLMs, RAG Architecture(Embeddings(Azure Ai Search, DataBricks) , Semantic Search Etc. – Must Have Production Grade solution architecture for RAG based AI Solutions – Good to have

You are a dynamic and tech-savvy Pre-Sales Consultant with exceptional deck creation and storytelling skills. Your primary responsibility will be to collaborate with Sales, Product, and Delivery teams in order to understand client needs, shape GenAI-powered solutions, and present them in compelling business terms to enterprise clients. Your main responsibilities include designing and presenting GenAI use cases such as document summarization, knowledge assistants, intelligent search, and content generation. You will be expected to build high-impact decks and demos to effectively communicate value propositions to both technical and non-technical audiences. Collaboration with Data Scientists, Engineers, and Product Managers to scope solutions, and create proposal responses will be crucial. It is essential to stay updated on the latest LLMs, APIs, and tools like OpenAI, LangChain, Anthropic, Hugging Face, etc. Furthermore, you will engage in conducting proof of concept (POC) discussions and mapping customer problems to GenAI capabilities. Supporting go-to-market initiatives, GenAI workshops, and client enablement programs will also be part of your role. To qualify for this position, you should have 5-10 years of experience in Pre-Sales, Solution Consulting, or AI Product roles. A strong understanding of LLMs, RAG architecture, vector databases, and prompt engineering is required. Demonstrated experience with AI/ML use cases, particularly in the NLP/GenAI space, is preferred. You must possess the proven ability to create visually compelling decks and pitch narratives using PowerPoint, Canva, or equivalent tools. Excellent client-facing communication and presentation skills are essential, along with experience working with cross-functional teams and influencing C-level stakeholders. Join Tredence, a pioneering company dedicated to transforming data into actionable insights for Fortune 500 clients across various industries. With a presence in 5 countries and a mission to be the world's most indispensable analytics partner, Tredence combines deep domain expertise with advanced AI and data science to drive unparalleled business value. Embrace this opportunity to be part of an innovative journey at Tredence.,

As a S&C GN AI - Insurance AI Generalist Consultant at Accenture, you will play a key role in driving strategic initiatives, managing business transformations, and leveraging industry expertise to create value-driven solutions. Your responsibilities will include providing strategic advisory services, conducting market research, and developing data-driven recommendations to enhance business performance. You will have the opportunity to work with Accenture's Global Network, a unified powerhouse that combines the capabilities of Strategy & Consulting with Data and Artificial Intelligence. This role will involve architecting, designing, building, deploying, delivering, and monitoring advanced analytics models, including GenAI, to solve client problems. You will also be responsible for developing functional aspects of Generative AI pipelines and interfacing with clients to understand engineering and business problems. The ideal candidate for this role will have at least 5 years of experience in data-driven techniques, including exploratory data analysis, data pre-processing, machine learning, and visualization. A Bachelor's or Master's degree in Mathematics, Statistics, Economics, Computer Science, or a related field is required. Strong analytical, problem-solving, and communication skills are essential, along with proficiency in programming languages such as Python, PySpark, SQL, and Scala. Experience in implementing AI solutions for the Insurance industry and production-grade integration of AI/ML pipelines is preferred. Additionally, familiarity with Azure, AWS, or Databricks tools, as well as GenAI, LLMs, RAG architecture, and Lang chain frameworks, would be beneficial. Strong communication, collaboration, and presentation skills are necessary to effectively convey complex data insights and recommendations to clients and stakeholders. Joining Accenture offers you the opportunity to work on innovative projects, experience career growth, and gain leadership exposure. If you thrive in a fast-paced, dynamic environment and are passionate about leveraging Generative AI to drive business success, this role is perfect for you.,

The primary responsibility of this role is to lead the strategic design and implementation of enterprise-scale AI GenAI solutions. You will be required to drive architectural decisions across complex AI systems and serve as a trusted advisor to C-suite executives, leading AI transformation initiatives. Your key responsibilities will include developing AI technology adoption strategies, creating AI architecture and roadmap, conducting AI data readiness assessments, designing AI governance frameworks and risk assessments, building GenAI pipeline design and LLM integration architecture, formulating innovation strategies for emerging AI technologies, ensuring understanding of AI ethics and responsible AI practices, managing change for AI adoption across organizations, and delivering technical presentations to diverse stakeholder groups. In terms of technical requirements, you must possess expert level proficiency in Python for AI, be well-versed in AI GenAI libraries frameworks, have experience in production scale LLM implementation and fine-tuning, be skilled in RAG architecture design and vector database optimization, demonstrate expertise in multi-cloud AI services such as AWS Bedrock, Azure, OpenAI, or Vertex AI, have knowledge of Agentic AI systems and multi-agent orchestration, understand enterprise data architecture and distributed systems, be competent in MLOps LLMOps pipeline design and automation, and have experience with microservices and API-first architecture. Additional responsibilities may include obtaining AWS, Azure, GCP AI certifications, showcasing executive communication and presentation skills, possessing industry domain expertise in areas like Healthcare, FinTech, or Manufacturing, demonstrating business acumen and strategic planning capabilities, and having at least 3 years of client-facing advisory experience. Preferred skills for this role include expertise in retrieval augmented generation (RAG), support vector machines, AWS databases, traditional AI/ML solution architecture and design, and proficiency in cloud platforms.,

As a Senior AI Developer/ AI Architect in the AI team, you will have the opportunity to collaborate with and mentor a team of developers. Your primary focus will be on the Fusion AI Team and its AI engine AI Talos, where you will work with Large language models, simulations, and Agentic AI to deliver cutting-edge AI capabilities in the service management space. Your responsibilities will include developing intricate python-based AI code to ensure the successful delivery of advanced AI functionalities. Additionally, you will play a crucial role in team mentoring, guiding junior/mid-level developers in managing their workload efficiently and ensuring tasks are completed according to the product roadmap. Innovation will be a key aspect of your role, where you will lead the team in staying updated on the latest AI trends, especially focusing on large language models and simulations. Furthermore, you will be responsible for software delivery to customers while adhering to standard security practices. To qualify for this role, you should possess a degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Being Agile trained and practiced is also essential for this position. The ideal candidate will have at least 7 years of experience in developing AI/Data Science solutions, with a senior level of involvement. Proficiency in Python and its libraries such as Pydantic, Pytorch, Pyarrow, Scikit, Hugging Face, and Pandas is required. Extensive knowledge of AI models and usage, including Llama2, Mistral AI, training models for classification, and RAG architecture, is necessary. Experience as a full-stack developer and familiarity with tools like GitHub, Jira, Docker, as well as GPU-based services architecture and setup, are advantageous. In terms of competencies, strong interpersonal and communication skills are essential. You will collaborate with teams across the business to create end-to-end high-value use cases and effectively communicate with senior management regarding requirement deadlines. Your excellent collaboration and leadership skills will ensure that the team remains motivated and is working efficiently towards set targets. If you are ready to take on this challenging role and contribute to the advancement of AI technologies, we encourage you to apply now at Future@fusiongbs.com.,

The role of S&C GN AI - Insurance AI Generalist Consultant at Accenture Global Network involves driving strategic initiatives, managing business transformations, and leveraging industry expertise to create value-driven solutions. As a Team Lead/Consultant at Bengaluru, BDC7C location, you will provide strategic advisory services, conduct market research, and develop data-driven recommendations to enhance business performance. In this position, you will be part of a unified powerhouse that combines the capabilities of Strategy & Consulting with Data and Artificial Intelligence. You will work on architecting, designing, building, deploying, delivering, and monitoring advanced analytics models, including Generative AI, for various client problems. Additionally, you will develop functional aspects of Generative AI pipelines and interface with clients to understand engineering/business problems. The ideal candidate for this role should have 5+ years of experience in data-driven techniques, a Bachelor's/Master's degree in Mathematics, Statistics, Economics, Computer Science, or a related field, and a solid foundation in Statistical Modeling and Machine Learning algorithms. Proficiency in programming languages such as Python, PySpark, SQL, Scala is required, as well as experience implementing AI solutions for the Insurance industry. Strong communication, collaboration, and presentation skills are essential to effectively convey complex data insights and recommendations to clients and stakeholders. Furthermore, hands-on experience with Azure, AWS, or Databricks tools is a plus, and familiarity with GenAI, LLMs, RAG architecture, and Lang chain frameworks is beneficial. This role offers an opportunity to work on innovative projects, career growth, and leadership exposure within Accenture, a global community that continually pushes the boundaries of business capabilities. If you are a motivated individual with strong analytical, problem-solving, and communication skills, and the ability to thrive in a fast-paced, dynamic environment, this role provides an exciting opportunity to contribute to Accenture's future growth and be a part of a vibrant global community.,

Business Analyst Lead | GenAI Strategy & Implementation | 7-10 Years of Experience Locations Open To: Gurgaon | Noida Seasoned Business Analyst Lead with 7-10 years of experience driving the adoption and operationalization of Generative AI technologies within enterprise environments. Proven ability to bridge the gap between cutting-edge innovationssuch as LLMs, RAG systems, and AI agentsand measurable business value. GenAI & Technical Expertise: Hands-on with LangChain, LlamaIndex, and RAG architectures; experience fine-tuning models using LoRA and working with vector DBs. Proficient in GenAI platforms including Azure OpenAI Studio, GCP Vertex AI, and Hugging Face. Familiar with synthetic data tools (Gretel, Mostly AI); skilled in validating data quality using SQL and Python. Business Analysis & Use Case Delivery: Skilled in identifying high-impact GenAI use cases across domains like content automation, customer service, and synthetic data generation. Expert in user story mapping for complex agent workflows (AutoGen, CrewAI) and BPMN process modeling for AI-human interaction. Conducts detailed cost-benefit analyses (e.g., GPT-4 Enterprise vs. open-source models) to inform build-vs-buy decisions. Requirements Engineering & Governance: Defines GenAI-specific NFRs including hallucination thresholds, latency SLAs, and ethical safeguards (bias, PII). Documents prompt engineering playbooks and iteration workflows to support scalable solution development. Establishes performance frameworks: token cost metrics, user trust scores, model drift alerts, and audit mechanisms aligned to regulatory standards (e.g., EU AI Act). Stakeholder Enablement: Converts technical GenAI potential into tangible business outcomes—such as reducing support costs by 30% or halving contract turnaround times with AI tools. Manages stakeholder expectations around the probabilistic nature of GenAI, implementing robust fact-checking and feedback loops.

Job Description Job Title: Data Engineer Your Role: Design, create, test and maintain data pipeline architecture in collaboration with the Data Architect. Build the infrastructure required for extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies. Support the translation of data needs into technical system requirements. Support in building complex queries required by the product teams. Build data pipelines that clean, transform, and aggregate data from disparate sources. Develop, maintain and optimize ETLs to increase data accuracy, data stability, data availability and pipeline performance. Engage with Product Management and Business to deploy and monitor products/services on cloud platforms. Stay up to date with advances in data persistence and big data technologies and run pilots to design the data architecture to scale with the increased data sets of consumer experience. Handle data integration, consolidation and reconciliation activities for digital consumer / medical products. You're the right fit if: Masters or Ph.D. in Computer Science, Electrical, Electronics Engineering or related field. Min. 4 yrs of experience in AI & Data Science field. Understanding of the state-of -the-art Computer vision, NLP, GenAI for Imaging algorithms. Experience in using GenAI techniques like Agentic frameworks, Auto encoders, Transformers, LLMs, RAG Architecture. Experience in CV based Deep Learning models like Unet, YOLO etc. Experience with Machine learning and deep learning frameworks like PyTorch , Tensorflow, OpenCV. Experience in Cloud deployment of AI algorithms. Application knowledge on Statistics skills such as distributions, statistical testing, regression, etc. Experience with one or more analytic software tools or languages like Python. Strong analytical, problem solving and communication skills. About Philips We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others. u2022 Learn more about . u2022 Discover . u2022 Learn more about . If youu2019re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care . #DIW #LI-PHILIN #PersonalHealth

Urgent! Need to close in 3 days. Hiring Senior Trainer to lead AI Agent, LLM, and automation training. Must have strong Python, MERN, LangChain, RAG, vector DBs, and hands-on dev skills. Training + dev role. Start sending resumes ASAP!

You are an experienced OCI AI Architect who will be responsible for leading the design and deployment of Gen AI, Agentic AI, and traditional AI/ML solutions on Oracle Cloud. Your role will involve a deep understanding of Oracle Cloud Architecture, Gen AI, Agentic and AI/ML frameworks, data engineering, and OCI-native services. The ideal candidate will possess a combination of deep technical expertise in AI/ML and Gen AI over OCI along with domain knowledge in Finance and Accounting. Your key responsibilities will include designing, architecting, and deploying AI/ML and Gen AI solutions on OCI using native AI services, building agentic AI solutions using frameworks such as LangGraph, CrewAI, and AutoGen, leading the development of machine learning AI/ML pipelines, and providing technical guidance on MLOps, model versioning, deployment automation, and AI governance. You will collaborate with functional SMEs, application teams, and business stakeholders to identify AI opportunities, advocate for OCI-native capabilities, and support customer presentations and solution demos. To excel in this role, you should have 10-15 years of experience in Oracle Cloud and AI, with at least 5 years of proven experience in designing, architecting, and deploying AI/ML & Gen AI solutions over OCI AI stack. Strong Python development experience, knowledge of LLMs such as Cohere and GPT, proficiency in AI/ML/Gen AI frameworks like TensorFlow, PyTorch, Hugging Face, and hands-on experience with OCI services are required. Additionally, skills in AI governance, Agentic AI frameworks, AI architecture principles, and leadership abilities are crucial for success. Qualifications for this position include Oracle Cloud certifications such as OCI Architect Professional, OCI Generative AI Professional, OCI Data Science Professional, as well as a degree in Computer Science or MCA. Any degree or diploma in AI would be preferred. Experience with front-end programming languages, Finance domain solutions, Oracle Cloud deployment, and knowledge of Analytics and Data Science would be advantageous. If you are a highly skilled and experienced OCI AI Architect with a passion for designing cutting-edge AI solutions on Oracle Cloud, we invite you to apply and join our team for this exciting opportunity.,

The opportunity: Hitachi Energy is seeking a highly motivated and skilled Business Analyst to support and drive the successful delivery of AI initiatives across the organization. This role will focus on identifying business opportunities, gathering and analyzing requirements, and collaborating with cross-functional teams to implement AI solutions using a variety of technologies and platforms. The ideal candidate will have a strong understanding of Business Requirements gathering concepts, excellent analytical skills, and experience working in complex industrial or energy environments. How you'll make an impact: Collaborate with business units to identify and prioritize AI use cases aligned with strategic goals. Conduct stakeholder interviews, workshops, and process analysis to gather detailed business requirements. Translate business needs into functional and technical specifications for AI solutions. Perform cost-benefit and impact analysis for proposed AI initiatives. Define and track KPIs to measure the success of AI implementations. Work closely with data scientists, AI engineers, and IT teams to ensure business requirements are accurately implemented. Support the development and deployment of AI Solutions using platforms such as Microsoft Azure AI. Assist in data preparation, validation, and governance activities. Ensure ethical AI practices and compliance with data privacy regulations. Prepare and present business cases, project updates, and post-implementation reviews liaising with the different vendor teams. Facilitate change management and user adoption of AI solutions. Maintain comprehensive documentation including business requirements, process flows, user stories, change requests, etc. Identify opportunities for process automation and optimization using AI technologies. Responsible to ensure compliance with applicable external and internal regulations, procedures, and guidelines. Living Hitachi Energy's core values of safety and integrity, which means taking responsibility for your actions while caring for your colleagues and the business. Your background: Bachelor's or Master's degree in Business, Engineering, Computer Science, or related field. Minimum 8 years of overall experience. 4+ years of experience as a Business Analyst, preferably in the energy or industrial sector. 2+ years of experience working on AI/ML projects. Strong understanding of AI/ML concepts, data lifecycle, and cloud platforms. Familiarity with tools such as Power BI, Azure DevOps, JIRA, Confluence. Experience with AI applications and solutions (e.g., Gen AI based Chatbots, RAG architecture, etc.). Certifications in Business Analysis (CBAP, PMI-PBA) or AI platforms (Azure AI Engineer, AWS Machine Learning). Proficiency in both spoken & written English language is required.,

We are seeking a dynamic and technically strong AI Lead with 68 years of industry experience, including a minimum of 5 years in AI/ML and Conversational AI technologies, with a specific focus on Microsofts AI ecosystem. The ideal candidate will lead the design, development, and delivery of intelligent solutions using Azure OpenAI, Copilot Studio, Microsoft Bot Framework, and AI Foundry. The individual will act as a hands-on technical lead, collaborating closely with product teams, architects, and business stakeholders to build impactful AI-powered copilots, chatbots, and enterprise automation solutions. Key Responsibilities: Lead end-to-end technical implementation of AI-driven projects using Microsoft AI tools: Azure OpenAI, Copilot Studio, and Bot Framework. Design and develop intelligent copilots, multi-turn chatbots, and custom GPT solutions integrated within enterprise tools such as Microsoft Teams, SharePoint, and Dynamics 365. Translate business requirements into technical architecture and AI flows using OpenAI APIs, prompt engineering, and integration with enterprise systems. Leverage AI Foundry to manage the AI lifecycle including model selection, deployment, monitoring, and optimization. Architect AI/ML solutions that use Retrieval-Augmented Generation (RAG), semantic search, and contextual memory frameworks (LangChain, Semantic Kernel, etc.). Collaborate with product owners and business analysts to identify high-value use cases and define solution roadmaps. Develop and execute POCs and MVPs with hands-on coding, configuration, and orchestration of LLMs and chatbot pipelines.Integrate with enterprise data sources via APIs, GraphQL, and Microsoft Graph to create holistic user experiences. Mentor junior developers and work with DevOps teams to ensure stable deployment, CI/CD, and performance monitoring. Create documentation and reusable components/templates for repeated use across the organization. Stay current on Microsofts AI advancements and recommend tools, features, or practices that improve time-to-value and performance. Must-Have Skills: 6-8 years of overall experience, including 5+ years in AI/ML or Conversational AI Deep hands-on knowledge of: Azure OpenAI services and APIs Copilot Studio for building Microsoft 365-integrated assistants Microsoft Bot Framework SDK/Composer for chatbot development Prompt engineering for LLM optimization Strong Python or Node.js development skills (for AI orchestration and integration) Experience with enterprise system integration using APIs (Microsoft Graph, REST, JSON, OAuth) Familiarity with Azure ML, Azure Cognitive Services, and Azure DevOps Ability to design RAG-based architectures, manage embeddings, and leverage vector databases (e.g., Azure AI Search) Strong understanding of natural language processing (NLP) and foundational models (GPT, BERT) Excellent communication, leadership, and stakeholder engagement capabilities Good-to-Have Skills: Experience with Semantic Kernel or LangChain Working knowledge of AI Foundry for orchestrating AI pipelines Familiarity with Copilot extensibility and Teams App Studio Exposure to M365 Copilot APIs and custom plugin creation Knowledge of Responsible AI, data security, and compliance principles Familiarity with containerized deployment (Docker, Kubernetes)Experience in building dashboards and analytics (Kibana, Grafana) to visualize bot usage and performance Basic understanding of Power Platform (Power Automate, Power Apps) and its integration with AI

We are hiring a Full Stack Developer with strong exposure to AI tools, APIs, and product development workflows. This role is for someone who can independently design, build, and deploy full-stack applications, and also integrate AI-powered components such as RSVP agents, recommendation systems, conversational flows, and automation tools. Responsibilities Build and maintain full-stack web apps using React, Node.js, Python Integrate AI/ML APIs like OpenAI, Cohere, LangChain, Pinecone, etc. Architect intelligent features using vector databases, RAG pipelines, and custom agent flows Work on both frontend + backend and own deployment, testing & CI/CD Collaborate closely with product & automation teams Must-Have Skills Strong proficiency in JavaScript/TypeScript, Python, Node.js Comfortable with NoSQL, PostgreSQL, Firebase, or Supabase Experience with API integrations, automation, and microservices Understanding of AI agentic flows, embeddings, and webhooks Experience deploying products on Vercel, Render, or similar Good to Have Familiarity with tools like LangChain, LlamaIndex, or OpenAI Assistants Working knowledge of Next.js, Tailwind CSS, and prompt engineering Work Culture Fully remote Flat structure Fast execution environment Product-first mindset

About the Role We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions. You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems. Responsibilities Generative AI Pipeline Development Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads. Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs. Build CI/CD pipelines with integrated prompt regression testing and version control. Use orchestration frameworks like LangChain or LangGraph for tool routing and multi-hop workflows. Monitor system performance using tools like Langfuse or Prometheus. Data and Document Ingestion Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data. Apply preprocessing pipelines for text, images, and code. Ensure data integrity, format consistency, and security across sources. AI Service Integration Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.). Build internal APIs for smooth backend-AI communication. Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets. Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy. Retrieval-Augmented Generation (RAG) Pipelines Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API). Design custom retrieval strategies for multi-modal or multi-source documents. Apply post-retrieval ranking using DPO or feedback-based techniques. Improve contextual relevance through re-ranking, chunk merging, and scoring logic. LLM Integration and Optimization Manage prompt engineering, model interaction, and tuning workflows. Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design. Optimize generation using temperature tuning, token limits, and speculative decoding. Integrate observability and cost-monitoring into LLM workflows. Backend Services Ownership Design and maintain scalable backend services supporting GenAI applications. Implement monitoring, logging, and performance tracing. Build RBAC (Role-Based Access Control) and multi-tenant personalization. Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production. Required Skills and Qualifications Education Bachelors or Masters in Computer Science, Artificial Intelligence, Machine Learning, or related field. Experience 5+ years of experience in AI/ML engineering with end-to-end pipeline development. Hands-on experience building and deploying LLM/RAG systems in production. Strong experience with public cloud platforms (AWS, Azure, or GCP). Technical Skills Proficient in Python and libraries such as Transformers, SentenceTransformers, PyTorch. Deep understanding of GenAI infrastructure, LLM APIs, and toolchains like LangChain/LangGraph. Experience with RESTful API development and version control using Git. Knowledge of vector DBs (Qdrant, FAISS, Weaviate) and similarity-based retrieval. Familiarity with Docker, Kubernetes, and scalable microservice design. Experience with observability tools like Prometheus, Grafana, or Langfuse. Generative AI Specific Skills Knowledge of LLMs, VAEs, Diffusion Models, GANs. Experience building structured + unstructured RAG pipelines. Prompt engineering with safety controls, schema enforcement, and hallucination mitigation. Experience with prompt testing, caching strategies, output filtering, and fallback logic. Familiarity with DPO, RLHF, or other feedback-based fine-tuning methods. Soft Skills Strong analytical, problem-solving, and debugging skills. Excellent collaboration with cross-functional teams: product, QA, and DevOps. Ability to work in fast-paced, agile environments and deliver production-grade solutions. Clear communication and strong documentation practices. Preferred Qualifications Experience with OCR, document parsing, and layout-aware chunking. Hands-on with MLOps and LLMOps tools for Generative AI. Contributions to open-source GenAI or AI infrastructure projects. Knowledge of GenAI governance, ethical deployment, and usage controls. Experience with hallucination suppression frameworks like Guardrails.ai, Rebuff, or Constitutional AI. Shift Time: 2:30 PM to 11:30 PM IST Location-Remote,Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

About the Role We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions. You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems. Responsibilities Generative AI Pipeline Development Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads. Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs. Build CI/CD pipelines with integrated prompt regression testing and version control. Use orchestration frameworks like LangChain or LangGraph for tool routing and multi-hop workflows. Monitor system performance using tools like Langfuse or Prometheus. Data and Document Ingestion Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data. Apply preprocessing pipelines for text, images, and code. Ensure data integrity, format consistency, and security across sources. AI Service Integration Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.). Build internal APIs for smooth backend-AI communication. Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets. Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy. Retrieval-Augmented Generation (RAG) Pipelines Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API). Design custom retrieval strategies for multi-modal or multi-source documents. Apply post-retrieval ranking using DPO or feedback-based techniques. Improve contextual relevance through re-ranking, chunk merging, and scoring logic. LLM Integration and Optimization Manage prompt engineering, model interaction, and tuning workflows. Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design. Optimize generation using temperature tuning, token limits, and speculative decoding. Integrate observability and cost-monitoring into LLM workflows. Backend Services Ownership Design and maintain scalable backend services supporting GenAI applications. Implement monitoring, logging, and performance tracing. Build RBAC (Role-Based Access Control) and multi-tenant personalization. Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production. Required Skills and Qualifications Education Bachelors or Masters in Computer Science, Artificial Intelligence, Machine Learning, or related field. Experience 5+ years of experience in AI/ML engineering with end-to-end pipeline development. Hands-on experience building and deploying LLM/RAG systems in production. Strong experience with public cloud platforms (AWS, Azure, or GCP). Technical Skills Proficient in Python and libraries such as Transformers, SentenceTransformers, PyTorch. Deep understanding of GenAI infrastructure, LLM APIs, and toolchains like LangChain/LangGraph. Experience with RESTful API development and version control using Git. Knowledge of vector DBs (Qdrant, FAISS, Weaviate) and similarity-based retrieval. Familiarity with Docker, Kubernetes, and scalable microservice design. Experience with observability tools like Prometheus, Grafana, or Langfuse. Generative AI Specific Skills Knowledge of LLMs, VAEs, Diffusion Models, GANs. Experience building structured + unstructured RAG pipelines. Prompt engineering with safety controls, schema enforcement, and hallucination mitigation. Experience with prompt testing, caching strategies, output filtering, and fallback logic. Familiarity with DPO, RLHF, or other feedback-based fine-tuning methods. Soft Skills Strong analytical, problem-solving, and debugging skills. Excellent collaboration with cross-functional teams: product, QA, and DevOps. Ability to work in fast-paced, agile environments and deliver production-grade solutions. Clear communication and strong documentation practices. Preferred Qualifications Experience with OCR, document parsing, and layout-aware chunking. Hands-on with MLOps and LLMOps tools for Generative AI. Contributions to open-source GenAI or AI infrastructure projects. Knowledge of GenAI governance, ethical deployment, and usage controls. Experience with hallucination suppression frameworks like Guardrails.ai, Rebuff, or Constitutional AI. Experience and Shift Shift Time: 2:30 PM to 11:30 PM IST Location: Remote- Bengaluru,Hyderabad,Delhi / NCR,Chennai,Pune,Kolkata,Ahmedabad,Mumbai

Responsibilities: * Collaborate with dev team on API testing using GIT and CI/CD pipeline. * Develop automated tests with Python, PyTest, and frameworks.

Client Name: WIPRO Location: PAN INDIA Mode: Hybrid Job Description: Experience Required: 10+ years in AI/ML and Solution Architecture roles (Strong programming and system integration background required) Key Responsibilities: Troubleshoot and fix an existing BERT-integrated Outsystems+AMP solution, including scheduler code debugging. Translate complex business requirements into scalable and integrative AI/GenAI solutions. Architect and guide end-to-end implementation of AI systems aligned with Ericssons target architecture (e.g., TAMP). Lead architectural reviews and support development teams to ensure solution compliance. Manage risks associated with AI models via standard risk assessment protocols. Create, maintain, and own architectural documentation and blueprints. Present technical concepts and designs to stakeholders. Collaborate with cross-functional teams including development and platform teams for successful project delivery. Additional Responsibilities: Apply advanced ML algorithms to solve business problems and drive value. Build and deploy data science solutions at scale (AWS Sagemaker, Kubernetes, Docker). Stay updated with current AI trends and contribute to applied research, innovation, and IP creation (e.g., publications, patents). Deliver insights to business leaders to influence decision-making. Qualifications: Bachelors or Masters degree in Computer Science, Engineering, or related field. Proven track record in delivering AI/ML solutions (preferably in Finance domain). Recognized experience on data science platforms (e.g., Kaggle achievements is a plus). Strong coding background in Python and experience in cloud-native environments. Good to Have: Domain expertise in Finance. Contributions to open-source or research community (e.g., Kaggle, GitHub, papers).

Department/ Group : Advanced Rail Technology Job Description, duties & responsibilities Progress Rails data science team is looking for a motivated and talented Data Scientist who will primarily focus on developing Machine Learning/Artificial Intelligence based data models for condition-based monitoring of its assets. In this role, the candidate will contribute to the design, development and deployment of world class rail products and services vital to our customers needs. Reporting to the Director of Data Science, this role will enable innovative, strategic, and high-tech solutions for the rail industry through the application of specialized knowledge, skills, and abilities. Work involves independent judgement, problem solving skills, resourcefulness, teamwork, and creativity in ambiguous situations. A high degree of personal initiative is a prerequisite. Typical data science team efforts are a combination of some, or all the key job elements listed below. The ideal candidate is an experienced self-starter, strong attention to details, with excellent written and verbal skills. Enjoys working in a collaborative, fast-paced, environment and is willing to take on roles outside of comfort zone. Technical aptitude and being well versed in Machine Learning and Data Science tools and processes is a must. The role will work closely with the different engineering teams. Key Job Elements Contribute to the design, development, testing, and deployment of software systems and applications. Processing, cleansing, and verifying the integrity of data used for analysis. Apply Machine Learning and other advanced analytical techniques to develop models for condition-based monitoring of locomotive systems. Apply Natural Language Processing (NLP) and Large Language Model (LLM) to support text mining, document summarization and others. Understand the business needs and develop data-based solutions. Doing ad-hoc analysis and presenting results in a clear manner Supporting field reported issue resolution through data analysis System integration of Machine Learning models Mentor and assist data scientists providing technical assistance and direction as needed Technical Skill Experienced Data Scientist with 7+ years experience in Data Extraction, Data Modelling, Data Wrangling, Statistical Modeling, Data Mining, Machine Learning and Data Visualization. Expertise in transforming business resources and requirements into manageable data formats and analytical models, designing algorithms, building models, developing data mining, and reporting solutions that scale across a massive volume of structured and unstructured data. Proficiency in managing entire data science project life cycle and actively involved in all the phases of project life cycle including data acquisition, data cleaning, data engineering, features scaling, features engineering, testing and validation and data visualization. Expertise in applying Machine Learning algorithms (such as Regression Models, XGBoost, Neural Network, and others) for predictive analytics. Experience in Generative AI developing LLM based solutions for document search/summarization using RAG architecture. Expertise in applied statistics skills, such as distributions, statistical testing, regression, etc. Strong experience with Python, SQL, and R. Experience and knowledge of AWS cloud which includes Machine Learning related services, S3, Elastic search, Lambda, and others. Experience in integrating Machine Learning models into larger deployed systems. Proficiency in data visualization tools such as PowerBI, Python Matplotlib, R Shiny to create visually powerful and actionable interactive reports and dashboards. Experience in Natural Language Processing and Text Mining. Strong business sense and abilities to communicate data insights to both technical and non-technical clients. Competent to perform all job duties without close supervision. Desired : Rail industry experience Experience in developing models using telematics (sensor) data from equipment such as engines, machines, and others. Qualifications and Education Requirements B.S, M.S, or PhD degree in quantitative discipline such as data science, data analytics, computer science, engineering, statistics, mathematics, or other related degree. 7+ years of data science experience with B.S., or 5+ years of experience with Advanced degrees 7+ years of experience with Python, R, SQL, and relational data bases

Design and implement RAG-based solutions to enhance LLM capabilities with external knowledge sources Develop and optimize LLM fine-tuning strategies for specific use cases and domain adaptation Create robust evaluation frameworks for measuring and improving model performance Build and maintain agentic workflows for autonomous AI systems Collaborate with cross-functional teams to identify opportunities and implement AI solutions Required Qualifications: Bachelor's or Master's degree in Computer Science, or related technical field 3+ years of experience in Machine Learning/AI engineering Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow) Practical experience with LLM deployments and fine-tuning Experience with vector databases and embedding models Familiarity with modern AI/ML infrastructure and cloud platforms (AWS, GCP, Azure) Strong understanding of RAG architectures and implementation Preferred Qualifications: Experience with popular LLM frameworks (Langchain, LlamaIndex, Transformers) Knowledge of prompt engineering and chain-of-thought techniques Experience with containerization and microservices architecture Background in NLP and deep learning Background in Reinforcement Learning Contributions to open-source AI projects Experience with ML ops and model deployment pipelines Skills and Competencies: Strong problem-solving and analytical skills Excellent communication and collaboration abilities Experience with agile development methodologies Ability to balance multiple projects and priorities Strong focus on code quality and best practices Understanding of AI ethics and responsible AI development

Role Overview Join our Pune AI Center of Excellence to drive software and product development in the AI space. As an AI/ML Engineer, youll build and ship core components of our AI products—owning end-to-end RAG pipelines, persona-driven fine-tuning, and scalable inference systems that power next-generation user experiences. Key Responsibilities Model Fine-Tuning & Persona Design Adapt and fine-tune open-source large language models (LLMs) (e.g. CodeLlama, StarCoder) to specific product domains. Define and implement “personas” (tone, knowledge scope, guardrails) at inference time to align with product requirements. RAG Architecture & Vector Search Build retrieval-augmented generation systems: ingest documents, compute embeddings, and serve with FAISS, Pinecone, or ChromaDB. Design semantic chunking strategies and optimize context-window management for product scalability. Software Pipeline & Product Integration Develop production-grade Python data pipelines (ETL) for real-time vector indexing and updates. Containerize model services in Docker/Kubernetes and integrate into CI/CD workflows for rapid iteration. Inference Optimization & Monitoring Quantize and benchmark models for CPU/GPU efficiency; implement dynamic batching and caching to meet product SLAs. Instrument monitoring dashboards (Prometheus/Grafana) to track latency, throughput, error rates, and cost. Prompt Engineering & UX Evaluation Craft, test, and iterate prompts for chatbots, summarization, and content extraction within the product UI. Define and track evaluation metrics (ROUGE, BLEU, human feedback) to continuously improve the product’s AI outputs. Must-Have Skills ML/AI Experience: 3–4 years in machine learning and generative AI, including 18 months on LLM- based products. Programming & Frameworks: Python, PyTorch (or TensorFlow), Hugging Face Transformers. RAG & Embeddings: Hands-on with FAISS, Pinecone, or ChromaDB and semantic chunking. Fine-Tuning & Quantization: Experience with LoRA/QLoRA, 4-bit/8-bit quantization, and model context protocol (MCP). Prompt & Persona Engineering: Deep expertise in prompt-tuning and persona specification for product use cases. Deployment & Orchestration: Docker, Kubernetes fundamentals, CI/CD pipelines, and GPU setup. Nice-to-Have Multi-modal AI combining text, images, or tabular data. Agentic AI systems with reasoning and planning loops. Knowledge-graph integration for enhanced retrieval. Cloud AI services (AWS SageMaker, GCP Vertex AI, or Azure Machine Learning)

About the Role We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions. You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems. Responsibilities Generative AI Pipeline Development Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads. Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs. Build CI/CD pipelines with integrated prompt regression testing and version control. Use orchestration frameworks like LangChain or LangGraph for tool routing and multi-hop workflows. Monitor system performance using tools like Langfuse or Prometheus. Data and Document Ingestion Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data. Apply preprocessing pipelines for text, images, and code. Ensure data integrity, format consistency, and security across sources. AI Service Integration Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.). Build internal APIs for smooth backend-AI communication. Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets. Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy. Retrieval-Augmented Generation (RAG) Pipelines Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API). Design custom retrieval strategies for multi-modal or multi-source documents. Apply post-retrieval ranking using DPO or feedback-based techniques. Improve contextual relevance through re-ranking, chunk merging, and scoring logic. LLM Integration and Optimization Manage prompt engineering, model interaction, and tuning workflows. Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design. Optimize generation using temperature tuning, token limits, and speculative decoding. Integrate observability and cost-monitoring into LLM workflows. Backend Services Ownership Design and maintain scalable backend services supporting GenAI applications. Implement monitoring, logging, and performance tracing. Build RBAC (Role-Based Access Control) and multi-tenant personalization. Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production. Required Skills and Qualifications Education Bachelors or Masters in Computer Science, Artificial Intelligence, Machine Learning, or related field. Experience 5+ years of experience in AI/ML engineering with end-to-end pipeline development. Hands-on experience building and deploying LLM/RAG systems in production. Strong experience with public cloud platforms (AWS, Azure, or GCP). Technical Skills Proficient in Python and libraries such as Transformers, SentenceTransformers, PyTorch. Deep understanding of GenAI infrastructure, LLM APIs, and toolchains like LangChain/LangGraph. Experience with RESTful API development and version control using Git. Knowledge of vector DBs (Qdrant, FAISS, Weaviate) and similarity-based retrieval. Familiarity with Docker, Kubernetes, and scalable microservice design. Experience with observability tools like Prometheus, Grafana, or Langfuse. Generative AI Specific Skills Knowledge of LLMs, VAEs, Diffusion Models, GANs. Experience building structured + unstructured RAG pipelines. Prompt engineering with safety controls, schema enforcement, and hallucination mitigation. Experience with prompt testing, caching strategies, output filtering, and fallback logic. Familiarity with DPO, RLHF, or other feedback-based fine-tuning methods. Soft Skills Strong analytical, problem-solving, and debugging skills. Excellent collaboration with cross-functional teams: product, QA, and DevOps. Ability to work in fast-paced, agile environments and deliver production-grade solutions. Clear communication and strong documentation practices. Preferred Qualifications Experience with OCR, document parsing, and layout-aware chunking. Hands-on with MLOps and LLMOps tools for Generative AI. Contributions to open-source GenAI or AI infrastructure projects. Knowledge of GenAI governance, ethical deployment, and usage controls. Experience with hallucination suppression frameworks like Guardrails.ai, Rebuff, or Constitutional AI. Experience and Shift Experience: 5+ years Shift Time: 2:30 PM to 11:30 PM IST Location: Remote- Bengaluru,Hyderabad,Delhi / NCR,Chennai,Pune,Kolkata,Ahmedabad,Mumbai

Job Description: AI Engineer (RAG & Agentic AI Specialist) Work Location: Mumbai and Gurgaon Why would you like to join us? TransOrg Analytics specializes in Data Science, Data Engineering and Generative AI, providing advanced analytics solutions to industry leaders and Fortune 500 companies across India, US, APAC and the Middle East. We leverage data science to streamline, optimize, and accelerate our clients' businesses. Visit at www.transorg.com to know more about us. Job Objective To build enterprise-grade AI solutions that leverage Retrieval-Augmented Generation (RAG), Agentic AI frameworks, and large language models (LLMs). The candidate will be responsible for end-to-end solutioning using modern tools and platforms, interacting with business teams, and driving innovation within the Microsoft and Azure ecosystem. Essential Job Tasks Build and deploy scalable AI solutions, from RAG systems to complex agent-based architectures using LangChain, CrewAI, AutoGen, or low code solutions like Copilot Studio, Azure AI Foundry. Leverage MCP, A2A, Microsoft Flows, and M365 Copilot for reasoning-based process automation. Collaborate with business to gather requirements and recommend best fit AI solutions. Work with the cloud infra team to set up secure, scalable AI systems on Azure. Ensure production-readiness and scalability of deployed AI solutions. Areas of Responsibility End-to-end ownership of AI product delivery. Integration of AI agents and retrieval mechanisms. Enabling adoption and training of AI practice across the organization. Work Experience 2-5 years overall experience in AI/ML or related fields. 2-3 years hands-on experience in RAG and agentic AI solutions. Required Qualifications B.E./B.Tech or M.Sc./B.Sc. in Computer Science, Data Science, or related field. Education from Tier 1 institutes preferred. Generative AI certifications are an advantage. Technical Knowledge LangChain, CrewAI, AutoGen, Copilot Studio, Azure AI Foundry. Creation and deployment of APIs using FastAPI or similar frameworks. OpenAI, Hugging Face, GPT4 APIs. Azure cloud (preferred). Working knowledge of Databricks and Azure Synapse. Power Platform (Power Automate, M365 Copilot Chat). Agent workflow orchestration, prompt design, RAG architecture, vector databases.

5+ years in AI/ML engineering with 2+ years on LLM/agentic systems Deep expertise in prompt engineering, RAG architectures and vector databases Proficient in Python, cloud (AWS Preferred ), Docker/Kubernetes and CI/CD