The role
Job description
Job Description: GenAI Lead
Location - Noida
Experience - 11+ Years
Job Overview
We are looking for an experienced Generative AI (GenAI) Lead to lead the design, development, and deployment of enterprise-grade Generative AI and Agentic AI solutions. The ideal candidate will combine strong hands-on technical expertise with technical leadership, solution design, and team management capabilities.
The candidate will be responsible for translating business requirements into scalable AI solutions, leading engineering teams, establishing development best practices, and delivering production-ready applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and cloud-based AI platforms.
Key Responsibilities
Lead the end-to-end design, development, and deployment of Generative AI and Agentic AI applications.
Drive technical architecture and implementation of LLM-based solutions, RAG pipelines, intelligent chatbots, AI assistants, and autonomous AI agents.
Build and optimize RAG solutions using embeddings, vector databases, semantic search, document processing, and knowledge retrieval techniques.
Design and implement AI agent workflows using frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent.
Integrate foundation models and LLM APIs from OpenAI, Anthropic, Google, Meta, and cloud AI platforms.
Lead prompt engineering, context engineering, model evaluation, fine-tuning, and performance optimization.
Develop scalable backend services and APIs using Python, FastAPI, or equivalent frameworks.
Lead the implementation of enterprise AI solutions on AWS, Microsoft Azure, or Google Cloud Platform.
Establish best practices for LLMOps, MLOps, CI/CD, automated testing, deployment, monitoring, and observability.
Ensure AI solutions meet enterprise requirements for scalability, reliability, security, privacy, governance, and cost efficiency.
Implement safeguards against hallucinations, prompt injection, sensitive-data exposure, and unauthorized tool execution.
Collaborate with product managers, business stakeholders, architects, data engineers, and cloud/DevOps teams to deliver AI-driven business solutions.
Conduct code reviews, design reviews, technical assessments, and proof-of-concept development.
Mentor developers and engineers, provide technical guidance, and promote engineering excellence.
Monitor emerging GenAI technologies and recommend suitable tools, frameworks, and architectural patterns.
Engage with customers and stakeholders to understand business challenges, present technical solutions, and drive successful delivery.
Mandatory Technical Skills
1. Generative AI and LLMs
Strong hands-on experience with Generative AI, Large Language Models (LLMs), and foundation models.
Expertise in prompt engineering, context management, structured outputs, function calling, and tool integration.
Experience with OpenAI, Azure OpenAI, Amazon Bedrock, Google Vertex AI, Anthropic, or equivalent platforms.
Strong understanding of LLM capabilities, limitations, model selection, token management, and inference optimization.
2. RAG and Vector Databases
Hands-on experience designing and implementing production-grade Retrieval-Augmented Generation (RAG) applications.
Experience with document ingestion, chunking, embeddings, reranking, hybrid search, and retrieval optimization.
Knowledge of vector databases and search platforms such as FAISS, Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search.
Understanding of advanced RAG, knowledge graphs, and evaluation techniques is an advantage.
3. Agentic AI and Frameworks
Experience building AI agents, multi-agent workflows, and tool-using AI applications.
Hands-on expertise in LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or equivalent frameworks.
Understanding of agent orchestration, state management, memory, tool execution, workflow control, and human-in-the-loop mechanisms.
Experience integrating external systems, enterprise APIs, databases, and business applications with AI agents.
4. Programming and Application Development
Strong proficiency in Python and object-oriented programming.
Experience with FastAPI, Flask, REST APIs, microservices, and backend application development.
Knowledge of SQL, data processing, asynchronous programming, and application performance optimization.
Experience with Git, automated testing, code reviews, and software engineering best practices.
5. Cloud, DevOps, and Production Deployment
Experience deploying GenAI applications on AWS, Azure, or GCP.
Knowledge of Docker, Kubernetes, CI/CD pipelines, and infrastructure automation.
Experience with production monitoring, logging, tracing, and AI application observability.
Understanding of MLOps/LLMOps, model versioning, evaluation pipelines, and lifecycle management.
6. AI Security and Governance
Understanding of responsible AI, data privacy, access controls, and enterprise AI governance.
Experience implementing guardrails, content filtering, prompt-injection protection, and sensitive-data handling.
Ability to evaluate AI output quality, groundedness, hallucination rates, latency, and cost.
Knowledge of secure API integration and identity/access management.
Leadership Responsibilities
Lead and mentor a team of AI/ML engineers and GenAI developers.
Own technical delivery, solution quality, and adherence to architecture standards.
Break down business requirements into technical designs, development tasks, and delivery milestones.
Review code and architecture to ensure maintainability, scalability, performance, and security.
Coordinate with cross-functional teams to resolve technical dependencies and delivery challenges.
Drive technical innovation, reusable components, proof of concepts, and continuous improvement.
Communicate technical decisions, risks, and progress to business stakeholders and senior leadership.
Required Experience
8–12+ years of overall experience in software engineering, AI/ML, or technology development.
3+ years of hands-on experience in Generative AI, LLM-based applications, or related AI technologies.
Proven experience building and deploying production-ready GenAI solutions.
Strong experience in RAG, LLM integration, AI agents, Python, and cloud-based application development.
Demonstrated experience leading technical teams, reviewing implementations, and owning solution delivery.
The experience range may be adjusted based on the seniority and scope of the position.
Preferred Qualifications
Experience with enterprise AI platforms and reusable GenAI solution architectures.
Knowledge of model fine-tuning techniques, including LoRA/QLoRA and parameter-efficient fine-tuning.
Experience with multimodal AI, document intelligence, and knowledge graph-based applications.
Familiarity with AI evaluation frameworks such as RAGAS, DeepEval, or equivalent.
Experience with observability and evaluation tools such as LangSmith, Langfuse, or equivalent.
Relevant cloud, AI/ML, or Generative AI certifications.
Key Competencies
Strong technical leadership and problem-solving skills.
Ability to remain hands-on while guiding engineering teams.
Excellent communication and stakeholder management skills.
Strong ownership of technical quality, delivery timelines, and production stability.
Ability to translate business problems into scalable, secure, and cost-effective AI solutions.
Success Criteria
Successful delivery of scalable, secure, and production-ready GenAI applications.
Improved accuracy, relevance, reliability, and performance of LLM-based solutions.
Effective implementation of RAG and Agentic AI use cases.
Adoption of reusable frameworks, engineering standards, and AI governance practices.
Improved team productivity, solution quality, and delivery predictability.