106 Llms Jobs - Page 2

Experience inimplementing & designing data pipelines using Azure Data Factory Advanced knowledge of Azure Functions,Event Grid Data processing using Azure AI services,cognitive services,Azure cognitive search Stakeholder management,project management

We have an excellent opportunity with one of our prestigious clients for CMMI Level 5 Company Skills - Large Language Models Exp- 13 Years to 25 Years Locations - Pan India Candidate must have 15 years of regular education. Must have Relevant experience minimum 8 Years of Exp in Large Language Models with Machine Learning, Natural Language Processing (NLP), Deep Learning, GenAI , MLOps , Python, AWS, Azure, GCP . Kindly share below the following details as well . Full Name: Total experience: Relevant Experience: Current CTC: Expected CTC: Notice Period: Current location: Preferred Location: As an AI/ML Engineer, you will develop applications and systems utilizing AI tools, Cloud AI services, and GenAI models. Your role involves creating cloud or on-prem application pipelines with production-ready quality, incorporating deep learning, neural networks, chatbots, and image processing. Must Have Skills : Proficiency in Large Language Models. Strong understanding of deep learning frameworks such as TensorFlow or PyTorch, Machine Learning, Natural Language Processing (NLP), Deep Learning ,GenAI,MLOps ,Python. Experience with cloud platforms like AWS, Azure, or Google Cloud for deploying AI solutions. Thanks Anand anand@vuiinfotech.in

Role & responsibilities • Familiarity with Mainframe and Midrange platforms is essential for integrating Gen AI solutions into legacy enterprise systems. Candidate should understand data structures, interfaces, and operational workflows on platforms like IBM Z(Mainframe) and IBM I (Midrange). Experience in modernizing and bridging AI applications with these systems is a strong advantage. masters or PhD in Computer Science, Machine Learning, or related field (or equivalent experience) 5+ years in AI/ML development, with 3+ years focused on generative AI (LLMs, GANs, VAEs, etc.) Proven track record of deploying generative AI solutions in production environments mandatory technical skills: deep expertise in generative architectures (GPT, Transformers, Diffusion Models) Experience with NLP tools (Hugging Face, spaCy) and vector databases (Pinecone, FAISS) mandatory proficiency in Python, PyTorch/TensorFlow, and cloud platforms preferred: knowledge of multimodal AI (text-to-image, video synthesis) experience with reinforcement learning (RLHF) for model alignment preferred familiarity with AI ethics frameworks (e.g., AI Fairness 360) strong analytical and problem-solving skills, excellent communication and collaboration abilities, attention to detail and ability to work independently Interested candidate can share me resume in recruiter.wtr26@walikingtree.in

Job Title : AI Engineer Location: Chennai Experience: 5+ Years (Hands-On Data Science & AI) Employment Type: Full-Time Work type - Work from office. About the Role We are seeking a skilled AI Engineer with deep hands-on experience in ML, NLP, LLMs, GenAI, and Agentic AI to join our technology team focused on automating US mortgage loan processing workflows. This role involves designing intelligent, scalable solutions using cloud-native MLOps and advanced AI/LLM orchestration tools on Azure. Key Responsibilities Design and implement ML/NLP/GenAI pipelines for automating loan origination, underwriting, and document intelligence processes. Develop and deploy LLM-based solutions using tools like Ollama, OpenAI, HuggingFace, and integrate via LangChain or similar frameworks. Build and orchestrate Agentic AI systems (e.g., using LangChain Agents, AutoGen, CrewAI) to enable autonomous decision-making, task planning, and loan-processing agents. Fine-tune domain-specific LLMs and embeddings for intelligent document classification, summarization, and question answering. Develop end-to-end MLOps workflows for scalable training, testing, and monitoring of models. Deploy AI models and microservices using Azure (Azure ML, Functions, Blob Storage, App Services). Work with cross-functional teams to ensure compliance, explainability, and effectiveness of AI solutions. Leverage prompt engineering, RAG (Retrieval-Augmented Generation), and vector stores for contextual mortgage document workflows. Required Skills & Qualifications 5+ years of hands-on experience in Machine Learning, NLP, or LLM-based systems. Proven expertise with LLMs (e.g., OpenAI, Ollama, GPT-4, Mistral) and LangChain / AutoGen / Agentic AI design. Proficiency in Python, Scikit-learn, PyTorch/TensorFlow, Spacy, HuggingFace Transformers. Strong knowledge of Azure Cloud including Azure ML, Azure Functions, Azure DevOps. Experience with MLOps tools like MLflow, Azure ML pipelines, or Kubeflow. Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate). Experience deploying models into production-scale environments. Nice to Have Understanding of US mortgage or lending workflows (1003, 1008, bank statements, etc.). Experience with OCR, document intelligence tools (Azure Form Recognizer, Amazon Textract). Exposure to Agentic AI concepts such as autonomous agents, planning, memory chaining. Knowledge of privacy and compliance frameworks (HIPAA, SOC2) in AI deployments. What We Offer Opportunity to lead GenAI and Agentic AI initiatives for mortgage automation. Access to top-tier tools, frameworks, and cloud platforms. Remote flexibility and career growth in an innovation-first team. Impactful work transforming legacy loan operations through AI. Interested candidates kindly share your resume to SGrace@ca-usa.com.

* Design, develop & maintain Python applications using Langchain & LLMS. * Optimize database performance with PostgreSQL & MySQL. Implement and integrate Retrieval-Augmented Generation (RAG) pipelines using LLMs Provident fund Health insurance

About this role: Wells Fargo is seeking a Principal Engineer to drive the design, development, and implementation of Artificial Intelligence (AI) enabled applications aligned to employee-facing lines of business in Enterprise Functions Technology (EFT). The EFT AI team will focus on building intelligent, efficient, and user-centric tools to accelerate the work of users in Finance, Human Resources, Risk, Audit, Legal, Public Affairs, Diverse Segments and EFT Platform Technology. In addition to hands-on development, this role will play a key part in evolving the AI operating model for both horizontal (cross product/platform) and vertical support (product specific). As a key technical contributor, you will collaborate with cross-functional teams to align solutions with the mission of transforming the Wells Fargo employee experience. This role will be a key contributor to shaping AI infrastructure, governance, and automation frameworks, working closely with cross-functional partners in technology, product management, and operations. The horizontal focus of this role will support AI platform scalability, reliability and AI governance, ensuring alignment across EFT, while the vertical focus will support specific applications and their integration within business domains. The successful candidate will have a deep expertise in AI engineering, cloud-based AI deployment, and AI model lifecycle management, along with experience in architecting AI solutions that align with responsible principles and enterprise business objectives. This is a unique opportunity to lead AI engineering efforts while shaping the AI operating model. In this role, you will: Design, develop and optimize Gen AI applications using agentic frameworks and tools Accelerate end to end solution delivery timelines by developing automated data, prompting and evaluation pipelines Streamline the enablement of AI solutions by building solution blueprints, re-usable patterns and identifying process improvements Lead AI engineering efforts that drive the design, development, scalability, and evolution of AI-powered products, ensuring AI adoption. Research and guide engineering efforts to solve complex engineering challenges and balance accuracy, latency and cost Evolve the AI operating model, ensuring seamless integration of AI solutions into business domains while providing horizontal AI governance and infrastructure support. Develop automated AI model monitoring frameworks, enabling continuous model updates, explainability, and performance tracking. Develop and scale AI platforms leveraging Large Language Model (LLM) services, real time analytics, AI automation, and intelligent decision-making. Act as an advisor and collaborate with leadership to integrate AI into existing enterprise systems and cloud platforms to implement innovative and significant business solutions. Drive cross-functional collaboration to define AI roadmaps, infrastructure strategies, and product enhancements, ensuring AI capabilities align with business AI strategies. Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that require vision, creativity, innovation, and advanced analytical and thinking. Maintain knowledge of industry best practices and new technologies and recommend innovations that enhance operations or provide a competitive advantage to the organization. Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership. Responsible for meticulous governance to address the unique risks posed by GenAI. Required Qualifications: 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education 7+ years of engineering experience building and supporting large scale customer facing products using modern technologies 3+ years building and delivering products using AI technologies. Desired Qualifications: Strong expertise in AI model development and deployment with a strong background in LLMs, generative AI, and AI-engineering. Deep experience with generative AI models, including model prompting, tuning, and safety best practices. Expertise in solution architecture and applying modular design techniques to agentic workflows Solid grasp of data and error analysis, identifying issues and patterns throughout the AI pipeline Strong expertise in test or eval driven development, ensuring robust and scalable AI software. Experience in backend application software development, with ability to quickly adapt to C#, and Python code bases. Strong understanding of Retrieval-Augmented Generation (RAG), knowledge graphs and agentic workflows. Deep knowledge of AI infrastructure, Generative AI Operations, and enterprise-scale AI adoption strategies. Familiarity with enterprise-scale software systems and their integration within large organizations. Passion for building AI solutions that deliver a seamless, end-user-focused experience. Experience in enterprise AI model lifecycle management, AI compliance, and risk mitigation strategies. Strong understanding of human centered AI design for workplace applications. Excellent collaboration, communication, and problem-solving skills. Job Expectations: Lead projects, teams, and serve as a peer mentor. Be Humble: You're smart yet always interested in learning from others Work Transparently: You always deal in an honest, direct, and transparent way. Take Ownership: You embrace responsibility and find joy in having the answers Learn More: You regularly self-educate and improve your skill set. Show Gratitude: You show appreciation and respect to those you work with.

Role & responsibilities Develop, test, and maintain backend services using Python and frameworks such as FastAPI or Flask . Implement and optimize LLM-based applications using models like OpenAI (GPT-4o) , Gemini , LLaMA , etc. Work on RAG implementations , including integration with vector databases and prompt engineering strategies. Design, build, and maintain database connectivity using SQL for infrastructure-level applications. Develop and deploy containerized applications using Docker , Git , GitHub , and integrate with CI/CD pipelines . Deploy and manage applications on cloud platforms ( AWS , Azure , GCP ). Ensure clean code practices including unit testing , error handling , Python best practices , and design patterns . Collaborate with cross-functional teams including Product, Data Science, and DevOps for end-to-end solution delivery. Preferred candidate profile Strong proficiency in Python programming . Familiarity with FastAPI , Flask , or similar web frameworks. Experience with SQL databases and connection management. Hands-on with LLMs and RAG workflows (OpenAI GPT, Gemini, LLaMA, etc.). Understanding of vector databases such as FAISS, Pinecone, or similar. Proficiency with Git , GitHub , and CI/CD pipelines . Experience with Docker for containerization. Exposure to any of the major cloud platforms AWS , Azure , or GCP . Ability to write unit test cases , implement robust error handling , and follow design patterns . Clear understanding of enterprise software development best practices . Someone who have experience working with Banking or Financial Services projects/clients. Immediate Joiner or someone serving notice period.

Role: ML Engineer. Exp : 5 Years to 10 Years Location : Hyderabad. Job Overview: Were seeking a ML Engineer / Data Scientist to architect agentic AI solutions and own the full ML lifecycle—from proof-of-concept to production. You’ll operationalize LLMs, build agentic workflows, implement MLOps best practices, and design multi-agent systems for cybersecurity tasks. Key Responsibilities: Operationalize large language models and agentic workflows (LangChain, LangGraph, LlamaIndex) to automate security decision-making and threat response. Design, deploy, and maintain multi-agent AI systems for log analysis, anomaly detection, and incident response. Build proof-of-concept GenAI solutions and evolve them into production-ready components on AWS (Bedrock, SageMaker, Lambda, EKS/ECS) using reusable best practices. Implement CI/CD pipelines for model training, validation, and deployment with GitHub Actions, Jenkins, and AWS CodePipeline. Manage model versioning with MLflow and DVC, set up automated testing, rollback procedures, and retraining workflows. Automate cloud infrastructure provisioning with Terraform and develop REST APIs and microservices containerized with Docker and Kubernetes. Monitor models and infrastructure through CloudWatch, Prometheus, and Grafana; analyze performance and optimize costs and SLA compliance. Collaborate with data scientists, application developers, and security analysts to integrate agentic AI into existing security workflows. Qualifications: Bachelor’s or master’s in computer science, Data Science, AI or related quantitative discipline. 4+ years of software development experience, including 3+ years building and deploying LLM-based/agentic AI architectures. In-depth knowledge of generative AI fundamentals (LLMs, embeddings, vector databases, prompt engineering, RAG). Hands-on experience with LangChain, LangGraph, LlamaIndex, Crew.AI or equivalent agentic frameworks. Strong proficiency in Python and production-grade coding for data pipelines and AI workflows. Deep MLOps knowledge: CI/CD for ML, model monitoring, automated retraining, and production-quality best practices. Extensive AWS experience with Bedrock, SageMaker, Lambda, EKS/ECS, S3 (Athena, Glue, Snowflake preferred). Infrastructure as Code skills with Terraform. Experience building REST APIs, microservices, and containerization with Docker and Kubernetes. Solid data science fundamentals: feature engineering, model evaluation, data ingestion. Understanding of cybersecurity principles, SIEM data, and incident response. Excellent communication skills for both technical and non-technical audiences. Preferred Qualifications: AWS certifications (Solutions Architect, Developer Associate). Nice to have Experience with Model Context Protocol (MCP) and RAG integrations. Nice to have Experience in Crew.AI Familiarity with workflow orchestration tools (Apache Airflow). Experience with time series analysis, anomaly detection, and machine learning.

Job Title: Generative AI Specialist Experience: Total 7+ Years | Relevant - 3 to 5 Years Location: Hyderabad Employment Type: Full-Time Job Description We are seeking a highly skilled and innovative Generative AI Engineer to join our team. The ideal candidate will have a strong background in designing, developing, and deploying AI models, particularly in the domain of generative AI and large language models (LLMs). Key Responsibilities Design & Development: Architect and build generative AI models, algorithms, and frameworks. Model Implementation: Integrate AI models into existing systems and applications. LLM Expertise: Work with tools like LangChain, Haystack, and apply prompt engineering techniques. Data Handling: Preprocess and analyze data for model training and evaluation. Cross-functional Collaboration: Partner with data scientists, product managers, and other stakeholders. Testing & Deployment: Evaluate model performance and deploy models to production. Monitoring & Optimization: Track model performance and continuously improve results. Research & Innovation: Stay updated with the latest advancements in generative AI. Required Skills Proficiency in Python for AI development. Strong understanding of Generative AI , NLP , and LLMs . Experience with RAG pipelines and vector databases . Familiarity with frameworks like LangChain , Haystack , and other open-source libraries. Knowledge of prompt engineering and tokenization . Experience in fine-tuning and integrating AI models in production. Excellent communication and problem-solving skills. Optional Skills Experience with cloud platforms (GCP, AWS, Azure). Familiarity with MLOps and DevOps practices. Why Join Us? Work on cutting-edge AI technologies. Collaborate with a passionate and talented team. Opportunity to innovate and shape the future of AI applications. How to Apply Interested candidates can apply directly through Naukri or send your updated resume to shilpa.shapur@Excelra.com

About the Team and Our Scope We are a forward-thinking tech organization within Swiss Re, delivering transformative AI/ML solutions that redefine how businesses operate. Our mission is to build intelligent, secure, and scalable systems that deliver real-time insights, automation, and high-impact user experiences to clients globally. You'll join a high-velocity AI/ML team working closely with product managers, architects, and engineers to create next-gen enterprise-grade solutions. Our team is built on a startup mindset - bias to action, fast iterations, and ruthless focus on value delivery. We're not only shaping the future of AI in business - we're shaping the future of talent. This role is ideal for someone passionate about advanced AI engineering today and curious about evolving into a product leadership role tomorrow. You'll get exposure to customer discovery, roadmap planning, and strategic decision-making alongside your technical contributions. Role Overview As an AI/ML Engineer, you will play a pivotal role in the research, development, and deployment of next-generation GenAI and machine learning solutions . Your scope will go beyond retrieval-augmented generation (RAG) to include areas such as prompt engineering, long-context LLM orchestration, multi-modal model integration (voice, text, image, PDF), and agent-based workflows. You will help assess trade-offs between RAG and context-native strategies, explore hybrid techniques, and build intelligent pipelines that blend structured and unstructured data. You'll work with technologies such as LLMs, vector databases, orchestration frameworks, prompt chaining libraries, and embedding models, embedding intelligence into complex, business-critical systems. This role sits at the intersection of rapid GenAI prototyping and rigorous enterprise deployment, giving you hands-on influence over both the technical stack and the emerging product direction. Key Responsibilities Build Next-Gen GenAI Pipelines : Design, implement, and optimize pipelines across RAG, prompt engineering, long-context input handling, and multi-modal processing. Prototype, Validate, Deploy : Rapidly test ideas through PoCs, validate performance against real-world business use cases, and industrialize successful patterns. Ingest, Enrich, Embed: Construct ingestion workflows including OCR, chunking, embeddings, and indexing into vector databases to unlock unstructured data. Integrate Seamlessly: Embed GenAI services into critical business workflows, balancing scalability, compliance, latency, and observability. Explore Hybrid Strategies: Combine RAG with context-native models, retrieval mechanisms, and agentic reasoning to build robust hybrid architectures. Drive Impact with Product Thinking : Collaborate with product managers and UX designers to shape user-centric solutions and understand business context. Ensure Enterprise-Grade Quality: Deliver solutions that are secure, compliant (e.g., GDPR), explainable, and resilient - especially in regulated environments. What Makes You a Fit Must-Have Technical Expertise Proven experience with GenAI techniques and LLMs , including RAG, long-context inference, prompt tuning, and multi-modal integration. Strong hands-on skills with Python , embedding models, and orchestration libraries (e.g., LangChain, Semantic Kernel, or equivalents). Comfort with MLOps practices , including version control, CI/CD pipelines, model monitoring, and reproducibility. Ability to operate independently, deliver iteratively, and challenge assumptions with data-driven insight. Understanding of vector search optimization and retrieval tuning. Exposure to multi-modal models Nice-To-Have Qualifications Experience building and operating AI systems in regulated industries (e.g., insurance, finance, healthcare). Familiarity with Azure AI ecosystem (e.g., Azure OpenAI, Azure AI Document Intelligence, Azure Cognitive Search) and deployment practices in cloud-native environments. Experience with agentic AI architectures , tools like AutoGen, or prompt chaining frameworks. Familiarity with data privacy and auditability principles in enterprise AI. Bonus: You Think Like a Product Manager While this role is technical at its core, we highly value candidates who are curious about how AI features become products . If you're excited by the idea of influencing roadmaps, shaping requirements, or owning end-to-end value delivery - we'll give you space to grow into it. This is a role where engineering and product are not silos . If you're keen to move in that direction, we'll mentor and support your evolution. Why Join Us You'll be part of a team that's pushing AI/ML into uncharted, high-value territory. We operate with urgency, autonomy, and deep collaboration. You'll prototype fast, deliver often, and see your work shape real-world outcomes - whether in underwriting, claims, or data orchestration. And if you're looking to transition from deep tech to product leadership , this role is a launchpad. Swiss Re is an equal opportunity employer . We celebrate diversity and are committed to creating an inclusive environment for all employees. Keywords: Reference Code: 134317

Job Description Summary role description: Hiring for a Solution Architect for an InsurTech platform provider, Life and Health Insurance. Company description: Our client is a VC-funded InsurTech platform company, providing software platforms for Life Insurance and Health Insurance companies across the globe. Leveraging their domain expertise, regulatory knowledge and technology experience, they architect innovative products and disrupt the Insurance value chain from Customer Acquisition to Engagement. Their products serve customers across the APAC region. Role details: Title / Designation : Solutions Architect Location: Pune/Mumbai Work Mode: Work from office Role & responsibilities: Define and evolve AI/ML architecture roadmap for FWA, IDP, and Agentic AI frameworks. Lead technical presentations and solution design sessions with customers. Design scalable architectures for multi-agent systems and autonomous decision-making. Drive innovation by evaluating emerging AI/ML technologies, especially AI agents. Architect cloud-native platforms supporting the complete AI/ML lifecycle. Provide technical leadership across product development and customer implementation. Collaborate with data scientists, engineers, and business stakeholders. Stay at the forefront of AI/ML innovations, particularly autonomous agents and LLMs. Establish and enforce technical standards and architectural guidelines. Candidate requirements: 10+ years in software architecture/system design in insurance domain, with 5+ years in AI/ML systems/platforms. Proven experience delivering large-scale AI/ML solutions, preferably with autonomous agents. Experience with cloud-native architectures (AWS, Azure, GCP), containerization (Docker, Kubernetes), and microservices. Deep expertise in AI/ML system architecture (model serving, MLOps/LLMOps pipelines, distributed computing). Strong understanding of Agentic AI, multi-agent systems, and LLMs (including LoRA, PEFT fine-tuning). Bachelor's or Master's in CS, SE, Data Science, or related technical field. Exceptional technical leadership and communication skills. Selection process: Interview with Senior Solution Architect Interview with CTO HR Discussion Check Your Resume for Match Upload your resume and our tool will compare it to the requirements for this job like recruiters do.

Key Responsibilities: LLM Integration & Development: Build and fine-tune LLMs for task-specific applications using techniques like prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and model adaptation. AI Agent Engineering: Design, develop, and orchestrate AI agents capable of reasoning, planning, tool use (e.g., APIs, plugins), and autonomous execution for user-defined goals. GenAI Use Case Implementation: Deliver GenAI-powered solutions such as chatbots, summarizers, document Q&A systems, assistants, and co-pilot tools using frameworks like LangChain or LlamaIndex. System Integration: Connect LLM-based agents to external tools, APIs, databases, and knowledge sources for real-time, contextualized task execution. Performance Tuning: Optimize model performance, cost-efficiency, safety, and latency using caching, batching, evaluation tools, and monitoring systems. Collaboration & Documentation: Work closely with AI researchers, product teams, and engineers to iterate quickly. Maintain well-structured, reusable, and documented codebases. Required Qualifications: 35 years of experience in AI/ML, with at least 12 years hands-on with GenAI or LLMs. Strong Python development skills and experience with ML frameworks (e.g., Hugging Face, LangChain, OpenAI API, Transformers). Familiarity with LLM orchestration, vector databases (e.g., FAISS, Pinecone, Weaviate), and embedding models. Understanding of prompt engineering, agent architectures, and conversational AI flows. Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field. Preferred Qualifications: Experience deploying AI systems in cloud environments (AWS/GCP/Azure) or with containerized setups (Docker/Kubernetes). Familiarity with open-source LLMs (LLaMA, Mistral, Mixtral, etc.) and open-weight tuning methods (LoRA, QLoRA). Exposure to RAG pipelines, autonomous agents (e.g., Auto-GPT, BabyAGI), and multi-agent systems. Knowledge of model safety, evaluation, and compliance standards in GenAI.

Key Responsibilities: LLM Integration & Development: Build and fine-tune LLMs for task-specific applications using techniques like prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and model adaptation. AI Agent Engineering: Design, develop, and orchestrate AI agents capable of reasoning, planning, tool use (e.g., APIs, plugins), and autonomous execution for user-defined goals. GenAI Use Case Implementation: Deliver GenAI-powered solutions such as chatbots, summarizers, document Q&A systems, assistants, and co-pilot tools using frameworks like LangChain or LlamaIndex. System Integration: Connect LLM-based agents to external tools, APIs, databases, and knowledge sources for real-time, contextualized task execution. Performance Tuning: Optimize model performance, cost-efficiency, safety, and latency using caching, batching, evaluation tools, and monitoring systems. Collaboration & Documentation: Work closely with AI researchers, product teams, and engineers to iterate quickly. Maintain well-structured, reusable, and documented codebases. Required Qualifications: 35 years of experience in AI/ML, with at least 12 years hands-on with GenAI or LLMs. Strong Python development skills and experience with ML frameworks (e.g., Hugging Face, LangChain, OpenAI API, Transformers). Familiarity with LLM orchestration, vector databases (e.g., FAISS, Pinecone, Weaviate), and embedding models. Understanding of prompt engineering, agent architectures, and conversational AI flows. Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field. Preferred Qualifications: Experience deploying AI systems in cloud environments (AWS/GCP/Azure) or with containerized setups (Docker/Kubernetes). Familiarity with open-source LLMs (LLaMA, Mistral, Mixtral, etc.) and open-weight tuning methods (LoRA, QLoRA). Exposure to RAG pipelines, autonomous agents (e.g., Auto-GPT, BabyAGI), and multi-agent systems. Knowledge of model safety, evaluation, and compliance standards in GenAI.

Key Responsibilities: LLM Integration & Development: Build and fine-tune LLMs for task-specific applications using techniques like prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and model adaptation. AI Agent Engineering: Design, develop, and orchestrate AI agents capable of reasoning, planning, tool use (e.g., APIs, plugins), and autonomous execution for user-defined goals. GenAI Use Case Implementation: Deliver GenAI-powered solutions such as chatbots, summarizers, document Q&A systems, assistants, and co-pilot tools using frameworks like LangChain or LlamaIndex. System Integration: Connect LLM-based agents to external tools, APIs, databases, and knowledge sources for real-time, contextualized task execution. Performance Tuning: Optimize model performance, cost-efficiency, safety, and latency using caching, batching, evaluation tools, and monitoring systems. Collaboration & Documentation: Work closely with AI researchers, product teams, and engineers to iterate quickly. Maintain well-structured, reusable, and documented codebases. Required Qualifications: 35 years of experience in AI/ML, with at least 12 years hands-on with GenAI or LLMs. Strong Python development skills and experience with ML frameworks (e.g., Hugging Face, LangChain, OpenAI API, Transformers). Familiarity with LLM orchestration, vector databases (e.g., FAISS, Pinecone, Weaviate), and embedding models. Understanding of prompt engineering, agent architectures, and conversational AI flows. Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field. Preferred Qualifications: Experience deploying AI systems in cloud environments (AWS/GCP/Azure) or with containerized setups (Docker/Kubernetes). Familiarity with open-source LLMs (LLaMA, Mistral, Mixtral, etc.) and open-weight tuning methods (LoRA, QLoRA). Exposure to RAG pipelines, autonomous agents (e.g., Auto-GPT, BabyAGI), and multi-agent systems. Knowledge of model safety, evaluation, and compliance standards in GenAI.

We are seeking a AI Product Manager to take ownership of defining, building, and scaling our core products. In this role, you will act as the bridge between technical teams (engineering, AI/ML) and business stakeholders.

Position - Lead Machine Learning Engineer- MLOps, VertexAI, LLMs, GenAI, ML Model Management Role Overview UPS Data Science and Machine Learning team is seeking a highly skilled and experienced Lead Machine Learning Engineer to manage our AI, ML, GenAI application focused on Cross Border logistics. This position leverages continuous integration and deployment of the best practices, including test automation and monitoring, to ensure successful deployment of optimal ML models and analytical systems. You will be responsible for the end-to-end lifecycle of AI models, from experimentation and fine-tuning to deployment and management in production. A strong background in prompt engineering and practical experience with either Google Cloud's Vertex AI platform is essential for this role. You will also provide technical leadership and mentorship to other members of the AI/ML team. Key Responsibilities Lead the development and deployment of generative AI solutions utilizing LLMs, SLMs, and FMs for various applications (e.g., content generation, chatbots, summarization, code generation, etc.). Architect and implement robust and scalable infrastructure for training, fine-tuning, and serving large-scale AI models, leveraging either Vertex AI. Drive the fine-tuning and adaptation of pre-trained models using proprietary data to achieve state-of-the-art performance on specific tasks. Develop and implement effective prompt engineering strategies to elicit desired outputs and control the behavior of generative models. Manage the lifecycle of deployed models , production support, including monitoring performance, identifying areas for improvement, and implementing necessary updates or retraining. Collaborate closely with cross-functional teams (e.g., product, engineering, research) to understand business requirements and translate them into technical solutions. Provide technical leadership and mentorship to junior machine learning engineers, fostering a culture of learning and innovation. Ensure the responsible and ethical development and deployment of AI models , considering factors such as bias, fairness, and privacy. Stay up to date with latest advancements in generative AI, LLMs, and related technologies, and evaluate their potential application within the company. Document technical designs, implementation details, and deployment processes. Troubleshoot and resolve issues related to model performance and deployment. Required Skills and Experience: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Minimum of 5-8 years of hands-on experience in building, deploying, and managing machine learning models in a production environment. Demonstrable experience in managing, deploying, and fine-tuning large language models (LLMs), small language models (SLMs), and foundation models (FMs). Significant hands-on experience with prompt engineering techniques for various generative AI tasks. Proven experience working with either Google Cloud's Vertex AI platform platform . including experience with their respective model registries, deployment tools, and MLOps features. Strong programming skills in Python and experience with relevant machine learning libraries (e.g., TensorFlow, PyTorch, Transformers). Experience with cloud computing platforms (beyond Vertex AI is a plus, e.g. Azure). Solid understanding of machine learning principles, deep learning architectures, and evaluation metrics. Excellent problem-solving, analytical, and communication skills. Ability to work independently and as part of a collaborative team. Experience with MLOps practices and tools for continuous integration and continuous delivery (CI/CD) of ML models is highly desirable. Experience with version control systems (e.g., Git). Bonus Points: Experience with model governance frameworks and implementing ethical AI practices. Experience with specific generative AI use cases relevant to Logistics industry. Publications or contributions to open-source projects, technical blogs, or industry conferences are considered a plus Familiarity with data engineering pipelines and tools. Familiarity with emerging trends in generative AI, reinforcement learning from human feedback (RLHF), and federated learning approaches.

Hi all, We are hiring for the role Generative AI Engineer Experience: 3 - 6 Years Location: Bangalore Notice Period: Immediate - 15 Days Skills: Generative AI Engineer Position Overview: We are looking for a Generative AI Engineer with expertise in Azure OpenAI and hands-on experience with models such as GPT-4o, GPT-o1, and open source LLMs like Llama, mistral. You will work on GenAI solutions development, RAG, fine-tuning, and deploying resources in Azure environment. Proficiency in prompt engineering, Python, PostgreSQL, FastAPI, Streamlit, Django and Angular is essential. This role also requires strong skills in AI models orchestration using intent mapping, Semantic Kernel or function calling, along with proficiency in presentation and public speaking. Key Responsibilities: • RAG, fine-tune, and deploy Azure OpenAI models (e.g., GPT-4o, GPT-o1) and other open-source large language models (LLMs). • Build AI-powered applications using frameworks such as FastAPI, Streamlit, Django, and Angular. • Design and execute AI workflows using tools like prompt flow, Semantic Kernel and implement function calling for complex use cases. • Conduct prompt engineering to improve model performance for specific business cases. • Visualize data and create user interaction insights using Power BI. • Ensure smooth deployment and maintenance of models on Azure cloud infrastructure, including scalability and optimization. • Prepare and deliver presentations, demos, and technical documentation to internal and external stakeholders. • Stay updated with advancements in generative AI, NLP, and machine learning to continuously improve models and methodologies. Required Skills & Qualifications: • Bachelors degree in Computer science, Artificial intelligence, Machine learning, or related field. • At least 2+ year of hands-on experience working on generative AI projects. • Strong expertise in Azure OpenAI models (GPT-4o, GPT-3.5, GPT-o1 etc.). • Proficient in Prompt Engineering, Python, Streamlit, Django, FastAPI, and Angular. • Basics of html, css, javascript, typescript and angular. • Basic understanding of neural networks, machine and transformer architectures. • Experience in retrieval-augmented generation (RAG) and fine-tuning Large language models. • Familiarity with AI model orchestration tools such as Semantic Kernel, intent mapping and function calling techniques. • Excellent public speaking and presentation skills to convey technical concepts to business stakeholders. • Azure Certified AZ900 or AI900 Preferred Qualifications: • Masters degree in Artificial Intelligence, Machine Learning, or related field. • At least 3+ years of experience working on generative AI, NLP, and machine learning projects. • Strong understanding of neural networks, machine learning and transformer architectures. • Implemented GenAI solutions in production. • Familiarity with Automotive Industry • Hands on experience in RAG, RAFT and optimized fine-tuning. • Azure Certified AI-102, DP-100, AZ-204 or DP-203 If you are interested drop your resume at mojesh.p@acesoftlabs.com Call: 9701971793

Develop Deep Learning models, focusing on NLP, Large Language Models (LLMs), and Generative AI. Expertise in NLP techniques (e.g., text classification, sentiment analysis, text generation).

Generative AI Engineer Python and FastAPI,deploying generative AI models (LLMs, GANs, VAEs) in production environmentsRAG, embeddings,RESTful APIsmanaging secure APIDocker and KubernetesSQL and NoSQL databases(AWS, Azure, GCP)

* If you are 5+ years experienced and Immediate joiner , please apply: https://docs.google.com/forms/d/e/1FAIpQLScorR1zEgu9FvDHO-4gjJEOPhk_2tJE6cgftARiy9rwcVcDHg/viewform 5+ years of experience as a backend or full-stack engineer with a strong backend focus Advanced proficiency in Python Practical experience integrating LLMs (e.g., RAG pipelines, agent frameworks, LangChain, LangGraph, or similar) Background in machine learning engineering is a strong plus Solid understanding of service architecture and production deployment workflows Hands-on LLM integration in production (not academic/chatbot-only experience) Expertise in designing production-grade APIs and backend services Strong knowledge of async workflows, deployment, observability, and performance *This is a software engineering role (not data science, analytics, or annotation)* Description: A complete remote role, you can use your own laptop. 8hrs/day, Mon-Fri required Eligibility: Candidates with 5+ years of relevant professional experience are eligible to apply.Must possess strong communication skills , both written and verbal.Should have proven expertise in the specific field or role being applied for. Interview Pattern: Screening Interview - 30 min on google meet Second Interview - 60 min Final Client Interview - 30 min Govt Id should be shown in all interviews and camera is a must. Timings: 8 hrs/day, Mon-Fri Salary will be competitive above the market standards and will be calculated on hourly basis. Role & responsibilities Preferred candidate profile : Immediate Joiners

Responsibilities • Design and develop AI models and algorithms • Build and train ML systems • Process large datasets • Deploy models to production • Collaborate with team to integrate AI solution • Ensure model accuracy • Stay updated with AI trends

Software Engineer L2 (Chatbot Engineering) Python and Django AWS RDBMS/NoSQL frontend, and LLMs, GenAI

AI Model Deployment & Integration: Deploy and manage AI/ML models, including traditional machine learning and GenAI solutions (e.g., LLMs, RAG systems). Implement automated CI/CD pipelines for seamless deployment and scaling of AI models. Ensure efficient model integration into existing enterprise applications and workflows in collaboration with AI Engineers. Optimize AI infrastructure for performance and cost efficiency in cloud environments (AWS, Azure, GCP). Monitoring & Performance Management: Develop and implement monitoring solutions to track model performance, latency, drift, and cost metrics. Set up alerts and automated workflows to manage performance degradation and retraining triggers. Ensure responsible AI by monitoring for issues such as bias, hallucinations, and security vulnerabilities in GenAI outputs. Collaborate with Data Scientists to establish feedback loops for continuous model improvement. Automation & MLOps Best Practices: Establish scalable MLOps practices to support the continuous deployment and maintenance of AI models. Automate model retraining, versioning, and rollback strategies to ensure reliability and compliance. Utilize infrastructure-as-code (Terraform, CloudFormation) to manage AI pipelines. Security & Compliance: Implement security measures to prevent prompt injections, data leakage, and unauthorized model access. Work closely with compliance teams to ensure AI solutions adhere to privacy and regulatory standards (HIPAA, GDPR). Regularly audit AI pipelines for ethical AI practices and data governance. Collaboration & Process Improvement: Work closely with AI Engineers, Product Managers, and IT teams to align AI operational processes with business needs. Contribute to the development of AI Ops documentation, playbooks, and best practices. Continuously evaluate emerging GenAI operational tools and processes to drive innovation. Qualifications & Skills: Education: Bachelor's or Master's degree in Computer Science, Data Engineering, AI, or a related field. Relevant certifications in cloud platforms (AWS, Azure, GCP) or MLOps frameworks are a plus. Experience: 3+ years of experience in AI/ML operations, MLOps, or DevOps for AI-driven solutions. Hands-on experience deploying and managing AI models, including LLMs and GenAI solutions, in production environments. Experience working with cloud AI platforms such as Azure AI, AWS SageMaker, or Google Vertex AI. Technical Skills: Proficiency in MLOps tools and frameworks such as MLflow, Kubeflow, or Airflow. Hands-on experience with monitoring tools (Prometheus, Grafana, ELK Stack) for AI performance tracking. Experience with containerization and orchestration tools (Docker, Kubernetes) to support AI workloads. Familiarity with automation scripting using Python, Bash, or PowerShell. Understanding of GenAI-specific operational challenges such as response monitoring, token management, and prompt optimization. Knowledge of CI/CD pipelines (Jenkins, GitHub Actions) for AI model deployment. Strong understanding of AI security principles, including data privacy and governance considerations.

NLP & Data Engineer - Risk Insights & Monitoring Engagement Type: Contract Contract Duration: 6 Months Domain: Banking / Finance Number of Openings: 1 We are seeking a highly skilled and experienced NLP & Data Engineer to contribute to our Risk Insights & Monitoring platform within the Banking/Finance sector. The ideal candidate will have a strong background in designing, implementing, and deploying state-of-the-art NLP models, coupled with robust data engineering capabilities and a passion for building high-quality, scalable software solutions. You will play a crucial role in leveraging natural language processing to extract valuable insights from unstructured data. Key Responsibilities: NLP Model Development & Implementation: Design and implement cutting-edge NLP models for tasks such as text classification, semantic search, sentiment analysis, named entity recognition (NER), and summary generation. Conduct thorough data preprocessing and feature engineering to enhance model accuracy and performance. Stay abreast of the latest developments in NLP and Machine Learning, integrating advanced techniques into our solutions. (Plus) Prior experience in agentic AI, Large Language Models (LLMs), prompt engineering, and generative AI. Data Engineering & Backend Development: Implement and deliver high-quality software solutions/components for the Credit Risk monitoring platform. Develop backend services and microservices using Java Spring Boot, J2EE, and REST APIs , ensuring high SLA for data availability and data quality. Proficiency in programming languages such as Python and Java , with experience in frameworks like TensorFlow, PyTorch, or Keras. Experience with data handling and processing tools like Pandas, NumPy, and SQL . Knowledge of SQL and PL/SQL (Oracle) and UNIX , including writing queries, packages, working with joins, partitions, looking at execution plans, and tuning queries. Cloud & DevOps: Experience building cloud-ready and migrating applications using Azure , with a strong understanding of Azure Native Cloud services, software design, and enterprise integration patterns. Experience using DevOps toolsets like GitLab and Jenkins . Build observability into solutions, monitor production health, assist in resolving incidents, and remediate the root cause of risks and issues. Strong desire to work with baked-in quality disciplines such as TDD (Test-Driven Development), BDD (Behavior-Driven Development), test automation, and DevOps principles. Collaboration & Mentoring: Collaborate closely with data scientists, software engineers, and product managers to align NLP projects with business objectives. Mentor developers, review code, and ensure adherence to best practices and standards. Share knowledge and expertise with colleagues, contribute to hiring, and actively participate in our engineering culture and internal communities. Quality & Compliance: Apply a broad range of software engineering practices, from analyzing user needs and developing new features to automated testing and deployment. Ensure the quality, security, reliability, and compliance of solutions by applying digital principles and implementing both functional and non-functional requirements. Stakeholder Management: Understand, represent, and advocate for client needs. Take ownership of assigned tasks through to ultimate resolution. Ensure accuracy and timeliness of delivering solutions using the best IT standards and practices. Expertise & Qualifications: Total Years of Experience: 8 to 10 years. Relevant Years of Experience: 5 to 8 years. Education: Bachelor of Engineering or equivalent. Domain Specific Experience: Ideally 8-10 years of experience in NLP-based applications focused on the Banking/Finance sector. Preference for experience in financial data extraction and classification. Mandatory Skills: NLP, Python . Desired Skills: SQL, Azure . Analytical & Communication: Strong analytical and problem-solving skills with the ability to think critically and creatively. Excellent verbal and written communication skills, with the ability to explain complex concepts to non-technical stakeholders. Learning Agility: Interested in learning new technologies and practices, reusing strategic platforms and standards, evaluating options, and making decisions with long-term sustainability in mind. Agile Mindset: Passion for and experience with Agile working practices. Additional Information: Approx. Vendor Billing Rate: INR 16,000 per day. Background Check: As per client policy.

Were seeking a forward-thinking Gen AI Engineer to design, implement, and optimize cutting-edge agentic AI solutions using frameworks such as Crew.ai , LangChain , and LangGraph . You will work at the intersection of LLMs , Retrieval-Augmented Generation (RAG) systems, and NLP , enabling impactful AI applications across diverse domains. Key Responsibilities Design and deploy autonomous AI agents and multi-agent systems using LLMs such as GPT-4o, Claude, and LLaMA, leveraging Crew.ai, LangChain (and LangGraph). Own the AI solution lifecycle , including data acquisition, model experimentation, fine-tuning, deployment, and production monitoring. Develop scalable AI backends and pipelines using Python with frameworks like PyTorch or TensorFlow, deploying via REST APIs (FastAPI) on cloud platforms like AWS or Azure. Implement RAG-based systems by integrating open-source LLMs (via Hugging Face or Ollama) with vector databases (e.g., Pinecone, ChromaDB) and structured data stores (SQL/NoSQL). Collaborate with cross-functional teams to ensure reliable, maintainable, and impactful AI solutions. Required Skills & Experience 2 to 5 years of experience in AI/ML, NLP, Generative AI, with a focus on agentic AI systems . Strong proficiency in Python and hands-on experience building RAG systems and deploying open-source LLMs. Experience developing AI-driven backends and services with a strong understanding of scalability and performance. Familiarity with vector search technologies, database integration, and cloud-native architectures. Excellent communication and collaboration skills; ability to work effectively in a team environment. Education B.Tech/B.E. or M.Tech/M.E. in Computer Science, AI/ML, or a related field. Role: Gen AI Engineer Designation: AI Developer Experience: 2-5 Years (AI/ML, NLP, Generative AI): Location: Pune