Senior Python Engineer (AI, ML & Agentic AI)

Role & responsibilities

About the Role

We're looking for a Senior AI/ML Engineer to design, build, and deploy production-grade AI-powered applications. The ideal candidate has strong Python engineering fundamentals combined with hands-on expertise in AI/ML, Generative AI, LLM frameworks, RAG pipelines, and AI agent systems. This role is primarily AI/ML focused, with a strong emphasis on building scalable, secure, and well-governed LLM-based solutions.

Key Responsibilities

- Design and develop AI/ML and AI-powered applications using Python, with FastAPI as the primary framework (Django and Flask also used)

- Build and optimize RAG (Retrieval-Augmented Generation) pipelines using LLMs, embeddings, and vector databases
- Design and execute evaluation frameworks for single-agent and multi-agent systems to measure task completion, reasoning quality, tool usage, and workflow efficiency.

- Develop GenAI solutions using LangChain, LangGraph, LlamaIndex

- Build AI agents and multi-agent workflows for business process automation

- Implement prompt engineering, retrieval evaluation, and hallucination reduction techniques

- Design and enforce LLM guardrails and governance frameworks (content filtering, output validation, safety policies, compliance, and audit logging)

- Build REST APIs and microservices to integrate AI/ML components with backend systems

- Deploy AI/ML services using AWS or GCP services, Docker, Kubernetes, and CI/CD pipelines

- Work with cloud AI platforms: AWS Bedrock, GCP Vertex AI, Azure OpenAI, or OpenAI APIs

- Monitor AI/ML model performance, cost, latency, and accuracy in production environments

Required Skills

- 5+ years of strong hands-on Python development experience

- Practical experience with Django, Django REST Framework, FastAPI, or Flask

- Solid understanding of AI/ML, Generative AI, LLMs, embeddings, RAG, and semantic search

- Hands-on experience with prompt engineering, RAG evaluation, re-ranking, and hallucination handling

- Experience implementing LLM guardrails and governance frameworks (safety, compliance, monitoring, and risk controls for AI systems)

- Experience with LLM frameworks: LangChain, LangGraph, LlamaIndex

- Experience with ML/AI libraries: Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers

- Experience with vector databases: Pinecone, FAISS, Chroma, Weaviate, Milvus, or pgvector

- Good knowledge of AWS Bedrock (model invocation, RAG, and integration with S3, Lambda, Glue, Athena, and OpenSearch)

- Strong understanding of REST API development, microservices architecture, and API security best practices

- Hands-on experience with AWS or GCP services, Docker, Kubernetes, and CI/CD pipelines (GitHub Actions/GitLab CI)

- Experience with relational and NoSQL databases: PostgreSQL, MySQL, MongoDB, or Redis

Preferred candidate profile