Job Description :

We are looking for an experienced GenAI Team Lead to lead the design, development, and deployment of enterprise Generative AI solutions. The ideal candidate will combine strong hands-on expertise in Python, LLMs, RAG, AI agents, prompt engineering, and cloud AI platforms with the ability to technically lead a team and drive solutions from PoC to production.

The role will involve working closely with Data Scientists, ML Engineers, software engineers, architects, and business stakeholders to build scalable, secure, and production-ready AI applications.

Key Responsibilities :

- Lead the design and development of Generative AI applications for enterprise use cases such as chatbots, summarization, intelligent automation, knowledge assistants, and decision support.

- Provide technical leadership to a team of AI/ML engineers and developers and guide them on architecture, development, testing, and deployment practices.

- Develop and integrate LLM-powered applications using Python and modern GenAI frameworks.

- Design and implement RAG pipelines, including document processing, chunking, embeddings, vector search, retrieval, reranking, and response generation.

- Work with vector databases and optimize retrieval strategies for accuracy, relevance, and performance.

- Develop and orchestrate AI agents and multi-step agentic workflows for enterprise automation.

- Apply prompt engineering techniques including zero-shot, few-shot, structured prompting, and prompt optimization.

- Work with frameworks and libraries such as LangChain, Hugging Face, OpenAI, Pydantic, Pandas, and NumPy.

- Design and implement APIs and backend services using FastAPI and Python.

- Develop, evaluate, and optimize LLM and ML solutions based on accuracy, latency, reliability, scalability, and cost.

- Deploy and manage AI solutions using cloud platforms such as Azure, AWS, or GCP, with hands-on exposure to services such as Azure OpenAI, Azure AI/ML, Azure Functions, AWS Bedrock, SageMaker, and Lambda.

- Integrate GenAI applications with enterprise APIs, data platforms, Snowflake, databases, and business workflows.

- Lead AI PoCs and convert successful solutions into scalable, production-ready implementations.

- Define technical standards and best practices for GenAI development, model evaluation, security, and deployment.

- Collaborate with Data Science, Data Engineering, ML Engineering, Product, and business teams to translate requirements into technical solutions.

- Monitor production AI applications and proactively address performance, reliability, security, and cost issues.

- Ensure AI solutions follow appropriate security, privacy, governance, and responsible AI practices.

- Conduct technical reviews, mentor team members, and contribute to hiring, capability development, and knowledge-sharing initiatives.

Required Skills & Experience :

- 6 - 8 years of overall experience, with at least 3+ years of hands-on experience in Generative AI / LLM-based solutions.

- Strong hands-on expertise in Python and AI application development.

- Strong experience with LLMs, RAG, embeddings, vector databases, and prompt engineering.

- Hands-on experience with AI agents / agentic workflows and enterprise AI automation.

- Strong experience with LangChain, OpenAI, Hugging Face, or equivalent GenAI frameworks.

- Experience building APIs and AI services using FastAPI and Pydantic.

- Good understanding of Machine Learning algorithms, model evaluation, training/inference pipelines, and MLOps concepts.

- Experience working with Azure, AWS, or GCP and deploying AI/ML solutions on cloud platforms.

- Hands-on exposure to services such as Azure OpenAI, Azure AI/ML, AWS Bedrock, SageMaker, or equivalent.

- Experience integrating AI applications with enterprise APIs, databases, data platforms, and business applications.

- Strong understanding of AI system design, scalability, security, observability, and cost optimization.

- Experience leading or mentoring engineers and taking ownership of technical delivery.

- Strong analytical, problem-solving, communication, and stakeholder-management skills.

Good to Have :

- Experience with Snowflake, Databricks, or enterprise data platforms.

- Experience with MLOps, model monitoring, evaluation frameworks, and AI governance.

- Experience with Docker, Kubernetes, and CI/CD for AI application deployment.

- Experience with Azure AI Foundry, AWS Bedrock, Amazon SageMaker, or Google Vertex AI.

- Relevant AI/ML or cloud certifications.

- Experience delivering GenAI solutions in enterprise production environments.