Lead Python & AI Engineer

Role Overview:

We are looking for a Lead Python & AI Engineer with strong expertise in Python backend development and hands-on experience in AI/ML and Generative AI systems. The ideal candidate will lead AI-driven product development, design scalable architectures, work directly with clients, and build production-ready AI solutions using LLMs, RAG, and modern cloud-native technologies.

You will play a key role in taking AI use cases from POC to enterprise-scale deployment.

Key Responsibilities

• Lead the design and development of AI-powered applications using Python.
• Architect and implement LLM-based systems (RAG, AI agents, prompt orchestration, vector search).
• Develop scalable backend services using FastAPI/Django.
• Integrate foundational models (OpenAI, Azure OpenAI, open-source LLMs).
• Design and implement data pipelines for AI workflows.
• Build and manage vector databases (Pinecone, Weaviate, FAISS, Azure AI Search, etc.).
• Optimize prompts, evaluate model performance, and implement guardrails.
• Lead client discussions for AI solutioning and technical architecture.
• Mentor junior engineers and establish AI engineering best practices.
• Deploy AI workloads on AWS/Azure/GCP using containerized environments (Docker/Kubernetes).
• Ensure security, governance, and scalability of AI systems.

Required Skills

1.Core Python & Backend

• Strong expertise in Python
• Hands-on experience with FastAPI or Django
• REST API development & microservices architecture
• SQL/NoSQL databases (PostgreSQL, MongoDB, Redis)

2.AI / ML / Generative AI (34+ Years )

• Experience building AI/ML-based production systems
• Hands-on experience with:
• LLMs (OpenAI, Azure OpenAI, HuggingFace, etc.)
• RAG (Retrieval Augmented Generation)
• Prompt engineering & evaluation
• Embeddings & vector search
• Experience with AI orchestration frameworks:
• LangChain / LlamaIndex / Semantic Kernel (any)
• Understanding of:
• Model evaluation
• AI observability
• Token optimization
• Responsible AI principles

3.Cloud & DevOps

• Experience deploying applications on AWS / Azure / GCP
• Containerization (Docker)
• Kubernetes (preferred)
• CI/CD pipelines
• Experience with private networking, secure API integration (good to have)

Good-to-Have Skills

• Experience building AI agents / multi-agent systems
• Experience with MLOps tools
• Exposure to data engineering pipelines
• Experience with streaming systems (Kafka, Pub/Sub)
• Frontend familiarity (React/JS) for AI dashboards
• Experience working in client-facing roles