38 Retrieval-Augmented Generation Jobs - Page 2

The JD for AI/Ml programmer is given below: Key Responsibilities: Design, develop, and deploy Generative AI models using state-of-the-art architectures (e.g., Transformers, Diffusion models). Build and fine-tune LLM-powered agents capable of multi-step reasoning, task planning, and tool use. Work with frameworks like LangChain, AutoGPT, BabyAGI, CrewAI , or similar agent orchestration tools. Integrate models with REST APIs, vector databases (e.g., Pinecone, FAISS, Chroma), and external systems. Optimize inference pipelines for performance, latency, and scalability. Collaborate with product managers and data scientists to prototype and productionize AI features. Stay updated on recent advancements in Generative AI and autonomous agents. Required Qualifications: 34 years of hands-on experience in Machine Learning / Deep Learning , with at least 1–2 years in Generative AI and/or AI Agents . Proficiency in Python and ML libraries such as PyTorch , TensorFlow , Transformers (Hugging Face) . Experience with LLM APIs (OpenAI, Claude, Mistral, etc.) and building LLM-based applications. Solid understanding of prompt engineering , fine-tuning , RAG (Retrieval-Augmented Generation) , and multi-modal learning . Familiarity with agent orchestration frameworks and LLM tool chaining . Strong problem-solving and communication skills. Preferred Qualifications: Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools (MLflow, Weights & Biases). Knowledge of Reinforcement Learning or Meta-learning for agent training. Experience contributing to open-source projects or published papers in the field of AI.

Senior Software Engineer/ LLM Ops Engineer External Description Description - External JD - What You Will Do Design, implement, and maintain LLM operations workflows using tools like Langfuse to monitor performance, track usage, and create feedback loops for continuous improvement Develop and maintain infrastructure-as-code for AI deployments using Terraform and AWS services (Lambda, SQS, API Gateway, OpenSearch, CloudWatch) Build and enhance monitoring, logging, and alerting systems to ensure optimal performance and reliability of our LLM infrastructure Collaborate with AI engineers to design and implement evaluation frameworks (including LLM-as-judge systems) to measure and improve model performance Manage prompt versioning, testing, and deployment pipelines through CI/CD and custom tooling Implement and maintain security guardrails for LLM interactions, ensuring compliance with best practices Create comprehensive documentation for LLM operations, including runbooks for production incidents Participate in on-call rotations to support mission-critical AI systems Drive innovation in LLM operations by researching and implementing best practices and emerging tools in the rapidly evolving GenAI space Deep understanding of prompt engineering strategies What You Will Bring To succeed in this role, you will need a combination of experience, technology skills, personal qualities, and education. Required Qualifications 3+ years of experience in DevOps, SRE, or similar roles, with at least 1 year specifically working with LLMs or AI systems in production Strong hands-on experience with AWS cloud services, particularly Bedrock, Lambda, SQS, API Gateway, OpenSearch, and CloudWatch Experience with infrastructure-as-code using Terraform, CloudFormation, or similar tools Proficiency in Python and experience building automation tooling and pipelines Familiarity with LangOps platforms such as Langfuse for LLM observability and evaluation Experience with CI/CD pipelines Knowledge of logging, monitoring, and alerting systems Understanding of security best practices for AI systems, including prompt injection mitigation techniques Excellent troubleshooting and problem-solving skills Strong communication skills and ability to work effectively with cross-functional teams Must be legally entitled to work in the country where the role is located Preferred Qualifications Experience with prompt engineering and testing tools like Promptfoo Familiarity with vector databases and retrieval-augmented generation (RAG) systems Knowledge of serverless architectures and event-driven systems Experience with AWS Guardrails for LLM security Background in data engineering or machine learning operations Understanding of financial systems and data security requirements in the finance industry Familiarity with implementing technical solutions to meet compliance requirements outlined in SOC2, ISAE 3402, and ISO 27001

We are looking for an AI Engineer (Senior Software Engineer). Interested candidates email me resumes on mayura.joshi@lionbridge.com OR WhatsApp on 9987538863 Responsibilities: Design, develop, and optimize AI solutions using LLMs (e.g., GPT-4, LLaMA, Falcon) and RAG frameworks. Implement and fine-tune models to improve response relevance and contextual accuracy. Develop pipelines for data retrieval, indexing, and augmentation to improve knowledge grounding. Work with vector databases (e.g., Pinecone, FAISS, Weaviate) to enhance retrieval capabilities. Integrate AI models with enterprise applications and APIs. Optimize model inference for performance and scalability. Collaborate with data scientists, ML engineers, and software developers to align AI models with business objectives. Ensure ethical AI implementation, addressing bias, explainability, and data security. Stay updated with the latest advancements in generative AI, deep learning, and RAG techniques. Requirements: 8+ years experience in software development according to development standards. Strong experience in training and deploying LLMs using frameworks like Hugging Face Transformers, OpenAI API, or LangChain. Proficiency in Retrieval-Augmented Generation (RAG) techniques and vector search methodologies. Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or Weaviate. Solid understanding of NLP, deep learning, and transformer architectures. Proficiency in Python and ML libraries (TensorFlow, PyTorch, LangChain, etc.). Experience with cloud platforms (AWS, GCP, Azure) and MLOps workflows. Familiarity with containerization (Docker, Kubernetes) for scalable AI deployments. Strong problem-solving and debugging skills. Excellent communication and teamwork abilities Bachelors or Masters degree in computer science, AI, Machine Learning, or a related field.

Role: AI/ML Engineer Has 4+ years of work experience , with at least 2+ years directly working on Generative AI and Agentic systems Knows how to use Large Language Models (LLMs) like GPT for real applications (not just research or theory) Has worked on multi-agent systems or autonomous AI agents using tools like CrewAI, AutoGen, or similar frameworks Can write smart prompts ( prompt engineering ), build apps with LLMs, and knows how to do RAG (Retrieval-Augmented Generation) Has done fine-tuning of models and built custom AI workflows Codes well in Python Has used ML frameworks like TensorFlow or PyTorch Familiar with tools like LangChain , and cloud/data platforms like AWS or Databricks Location: Bangalore/ Mumbai Shift Timing: 12:00 PM 9:00 PM Notice Period: 30 days or less Call Anumeha @ +91 6376649769/ Alfiya @ +91 8787064649

Lead AI Engineer About the Role: We are seeking an experienced AI specialist with a strong Computer Science/Engineering background to design, develop, and deploy advanced Generative AI-based solutions. In this role, you will build intelligent AI agents, leverage graph-based RAG techniques, and ensure robust production deployments to solve complex challenges in the Architecture, Engineering, and Construction (AEC) domain. Key Responsibilities Collaborate with stakeholders to align AI initiatives with business goals. Lead and mentor a team of ML engineers and data scientists. Design, develop, and deploy enterprise-grade RAG systems that deliver accurate, context-aware responses in production environments. Incorporate AI agents and graph-based techniques (e.g., GraphRAG) to enable enhanced contextual data retrieval and support complex query relationships. Implement robust evaluation frameworks to measure and enhance RAG system effectiveness. Create scalable pipelines for document processing, embedding generation, and knowledge base management. Build monitoring systems to track performance, detect issues, and ensure RAG system reliability. Architect cloud-native solutions that can scale to handle enterprise document volumes and query loads. Explore and integrate cutting-edge techniques in Generative AI, including but not limited to model fine-tuning. Qualifications & Skills A strong foundation in Computer Science or Engineering with 5-10 years' experience in NLP, Computer Vision, Deep Learning, Machine Learning, or related field. Extensive experience with LLMs and related technologies (e.g., embedding models, vector databases, etc.). Proven expertise in developing production-grade RAG systems. Knowledge of graph-based RAG techniques (e.g., Graph RAG or alternatives). Proficiency in Python and familiarity with Generative AI frameworks such as Hugging Face, Lang Chain, Llama Index, Prompt flow, Auto Gen, etc. Hands-on experience with cloud-based AI deployments (Azure preferred). Experience in LLM fine-tuning and optimization (e.g., LoRA) is a plus. Exposure to multi-modal RAG systems and LLMOps frameworks. Familiarity with deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks (e.g., PyTorch, Tensorflow, etc.).

Greetings from idexcel technologies: Role & responsibilities We are seeking a Data Scientist with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators for Tata Communications products and services. Detailed job description & Key Responsibilities: Develop, Test, and Deploy machine learning models for various business and Telco use cases. Perform data preprocessing, feature engineering and ML/DL model evaluation. Optimize and fine-tune models for performance and scalability. Good understanding of NLP concepts and projects involving entity recognition, text classification, and language modelling like GPT/Llama/Claude/Grok Build and refine RAG models to improve information retrieval and answer generation systems. Integrate RAG methods into existing applications to enhance data accessibility and user experience. Work closely with cross-functional teams including software engineers, product managers, and domain experts. Communicate technical concepts to non-technical stakeholders effectively. Document processes, methodologies, and model development for internal and external stakeholders. Skills: Strong knowledge of probability and statistics. Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools – PyTorch, Sci-kit, NumPy, Gen AI tools like Lang chain/llama Index Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines

ZENSAR - OPPORTUNITY FOR Gen AI with Python Engineer” Apply Here: - https://forms.office.com/r/nVP0Mg5eeE Dear Aspirant, Greetings from Zensar!! We are thrilled to offer you an excellent opportunity to join our team as a Gen AI with Python Engineer professional . Experience Required: 6 - 9 Years Location: Pune, Bangalore, Chennai, Hyderabad (Hybrid) LLM Applications & Agentic Frameworks Design and implement end-to-end LLM applications using OpenAI, Claude, Mistral, Gemini, or LLaMA on AWS, Databricks, Azure or GCP. Build intelligent, autonomous agents using LangGraph, AutoGen, LlamaIndex, Crew.ai, or custom frameworks. Develop Multi Model, Multi Agent, Retrieval-Augmented Generation (RAG) applications with secure context embedding and tracing with reports. Rapidly explore and showcase the art of the possible through functional, demonstrable POCs Advanced AI Experimentation Fine-tune LLMs and Small Language Models (SLMs) for domain-specific use. Create and leverage synthetic datasets to simulate edge cases and scale training. Evaluate agents using custom agent evaluation frameworks (success rates, latency, reliability) Evaluate emerging agent communication standards — A2A (Agent-to-Agent) and MCP (Model Context Protocol) Business Alignment & Cross-Team Collaboration Translate ambiguous requirements into structured, AI-enabled solutions. Clearly communicate and present ideas, outcomes, and system behaviors to technical and non-technical stakeholders Good-To-Have Microsoft Copilot Studio DevRev Codium Cursor Atlassian AI Databricks Mosaic AI Qualifications 6–9 years of experience in software development or AI/ML engineering At least 3 years working with LLMs, GenAI applications, or agentic frameworks. Proficient in AI/ML, MLOps concepts, Python, embeddings, prompt engineering, and model orchestration Proven track record of developing functional AI prototypes beyond notebooks. Strong presentation and storytelling skills to clearly convey GenAI concepts and value. Ability to independently drive AI experiments from ideation to working demo. Role & responsibilities Preferred candidate profile

Role: Tech lead/Architect Total exp 8-10 years Rel exp: 8 years Shift timings: 11 PM to 9 AM Bangalore, Hybrid Mode Description: Architect and implement scalable machine learning and deep learning models from scratch., Lead the development of information retrieval systems and relevance engineering solutions. Guide the team in implementing Retrieval-Augmented Generation (RAG) and vector-based search systems. Stay abreast of the latest advancements in AI, including LLMs, knowledge graphs, and generative AI. Spearhead the design, development, and deployment of cutting-edge AI solutions. You will lead a team of AI engineers, collaborate with cross-functional stakeholders, and drive innovation in machine learning, deep learning, and large language model applications. This role demands a strong foundation in AI technologies, hands-on experience in production-grade model deployment, and a strategic mindset to align AI initiatives with business goals. Hands-on experience with LangChain or similar frameworks,Skilled in building and tuning models using RAG, vector databases, and knowledge graphs

At least 3 years focused on AI & ML solutions. AI Engagements Strategic Consulting & Road mapping LLM/RAG Solution Design & Implementation Agentic Systems Hands-on Prototyping Thought Leadership

Role Overview We are seeking a highly skilled Agentic AI Developer to join our advanced AI engineering team. The ideal candidate will have hands-on experience in building intelligent multi-agent systems using cutting-edge frameworks and technologies in Generative AI, LLMOps, and AIOps. Must-Have Skills & Experience Backend Development Proficiency in Python Experience with FastAPI for backend services and API integration Generative AI & LLMOps Hands-on experience with Large Language Models (LLMs) , especially OpenAI models Expertise in Prompt Engineering and Retrieval-Augmented Generation (RAG) Agentic AI Systems Experience building multi-agent systems using the AutoGen framework Familiarity with Agentic AI design patterns , including: Reflection Planning Tool Use Multi-agent Collaboration Cloud & Data Services Experience with Azure AI Services Working knowledge of Vector Databases Proficiency in both SQL and NoSQL databases Preferred Skills Exposure to MLOps and AIOps practices Experience in Deep Learning frameworks Familiarity with Data Science workflows and pipelines Additional Information Strong problem-solving and system design skills Ability to work in a fast-paced, collaborative environment Excellent communication and documentation abilities

Mid-Level AI/ML Engr-design, develop, deploy ML model & AI solution, with focus on production-ready system.Collaborate closely with data scientists,software engineer, product manager to deliver AI capabilities that directly impact customer & business Perks and benefits Health insurance and wellness programs

Job Description:- Large Language Models (LLM): Experience with LangChain, LangGraph Proficiency in building agentic patterns like ReAct, ReWoo, LLMCompiler Multi-modal Retrieval-Augmented Generation (RAG): Expertise in multi-modal AI systems (text, images, audio, video) Designing and optimizing chunking strategies and clustering for large data processing Streaming & Real-time Processing: Experience in audio/video streaming and real-time data pipelines Low-latency inference and deployment architectures NL2SQL: Natural language-driven SQL generation for databases Experience with natural language interfaces to databases and query optimization API Development: Building scalable APIs with FastAPI for AI model serving Containerization & Orchestration: Proficient with Docker for containerized AI services Experience with orchestration tools for deploying and managing services Data Processing & Pipelines: Experience with chunking strategies for efficient document processing Building data pipelines to handle large-scale data for AI model training and inference AI Frameworks & Tools: Experience with AI/ML frameworks like TensorFlow, PyTorch Proficiency in LangChain, LangGraph, and other LLM-related technologies Prompt Engineering: Expertise in advanced prompting techniques like Chain of Thought (CoT) prompting, LLM Judge, and self-reflection prompting Experience with prompt compression and optimization using tools like LLMLingua, AdaFlow, TextGrad, and DSPy Strong understanding of context window management and optimizing prompts for performance and efficiency.

Title : Sr Generative AI Manager/ Sr Architect / Head of Manager Location : Remote | Gurugram Job Description: We are looking for a highly skilled Generative AI Engineer to lead the design and implementation of end-to-end GenAI pipelines. This role involves defining architecture, selecting appropriate models, and driving prompt engineering, Retrieval-Augmented Generation (RAG) setup, and fine-tuning strategies. You will also oversee model evaluation, optimization, and inference workflows to ensure high performance and relevance across use cases. Key Responsibilities: Define the overall architecture of Generative AI pipelines, including data flow and component integration. Evaluate and select appropriate foundational and fine-tuned models for different business needs. Lead prompt engineering and design of effective prompt templates to optimize model outputs. Set up and manage Retrieval-Augmented Generation (RAG) pipelines using vector databases and retrieval mechanisms. Oversee model fine-tuning, version control, and experiment tracking. Drive model evaluation efforts, including accuracy, latency, and cost-performance trade-offs. Optimize inference pipelines for scalability and integration with downstream applications. Collaborate with data scientists, MLOps, and product teams to deliver robust GenAI solutions.