AI Lead

About the Role We re looking for a forward-thinking AI Lead to architect and deliver cutting-edge Generative AI solutions. The ideal candidate combines deep Python expertise, mastery of scalable design principles, hands-on experience with LLMs, and strong knowledge of database and cloud systems. This is a leadership role for someone who thrives at the intersection of technology innovation and real-world impact. Key Responsibilities AI System Design: Lead the development of robust, scalable GenAI applications using Python and modern AI frameworks. LLM Integration: Work extensively with LLMs (GPT, Claude, LLaMA, Mistral) and tools like LangChain, LlamaIndex, LangGraph, and CrewAI for advanced conversational and decision-making workflows. Scalable Code: Write high-quality, modular, testable code with solid foundations in OOP and SOLID principles. Database Engineering: Design and manage relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) databases, and integrate with vector stores (FAISS, Pinecone, Chroma) for RAG systems. Cloud Deployment: Deploy and optimize AI workloads on AWS, Azure, or GCP using cloud-native services and serverless architectures. Team Leadership: Mentor AI/ML engineers, conduct architecture and code reviews, and foster a strong engineering culture. Cross-functional Collaboration: Partner with product, design, and DevOps teams to ship AI features that are reliable, secure, and efficient. Required Skills Experience Python Mastery: 3+ years of experience writing production-grade Python code. Software Architecture: Strong understanding of scalable systems, microservices, and API design. LLM/GenAI Stack: Deep hands-on experience with OpenAI APIs, LangChain, RAG pipelines, vector databases, and embedding strategies. Database Skills: Proficiency in data modeling, indexing, and optimization for MongoDB, PostgreSQL, MySQL, and vector stores. Cloud Platforms: Sound working knowledge of AWS, Azure, or GCP. Version Control CI/CD: Familiarity with Git workflows, Docker, pipelines, and modern deployment practices. Pydantic: Strong grasp of data validation and typing using Pydantic. Bonus Points / Good to Have Familiarity with Agentic AI frameworks like CrewAI, LangGraph, and multi-agent orchestration. Experience with FastAPI, asynchronous processing, or Kafka. Exposure to real-world use cases in BFSI, Healthcare, Government, or large enterprise systems.