AI/ML Developer

Job Overview

We are looking for a highly capable – Python & AI/ML with deep expertise in backend engineering, LLM-based applications, RAG architectures, and AI agent frameworks.

You will lead the design, development, and deployment of production-grade AI systems built on Python, modern LLM tooling, retrieval engines, embeddings, and vector databases. This is a hands-on leadership role focused on building scalable and intelligent AI products.

Key Responsibilities

• Lead the architecture and development of LLM-driven applications, AI agents, and RAG-based systems.
• Provide technical guidance, conduct code reviews, and mentor junior team members.
• Drive best practices in Python backend engineering, API development, and AI system design.

Backend Engineering (Python)

• Build and maintain backend services using FastAPI or Flask.
• Develop scalable API endpoints for AI applications, embeddings, and retrieval systems.
• Ensure backend code quality, modularity, performance, and maintainability.

LLMs, RAG, and AI Agent Development

• Build AI applications using: LangChain, LangGraph, Semantic Kernel, Haystack, LlamaIndex, AutoGen
• Develop autonomous or semi-autonomous AI agents with tool calling and workflow graphs.
• Implement Retrieval-Augmented Generation (RAG), embedding pipelines, chunking strategies, reranking, and grounding techniques.
• Work with OpenAI SDK and other LLM providers (Anthropic, Azure OpenAI, Cohere, etc.).
• Manage prompt engineering, prompt routing, safety guardrails, and evaluation metrics.

Data & Vector Search Engineering

• Build data pipelines for indexing, embeddings, and retrieval workflows.
• Work with SQL databases (PostgreSQL, MySQL, etc.) for metadata and application storage.

Required Qualifications

• Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or related fields.
• 8 TO 10 years of experience in Python backend development.
• Strong proficiency in FastAPI or Flask.
• Strong working knowledge of SQL databases (Postgres, MySQL, etc.).
• Hands-on expertise with vector databases: Redis, Postgres/pgvector, Elasticsearch, or Neo4j.
• Practical experience with FAISS for similarity search.
• Hands-on experience with modern LLM frameworks: LangChain, LangGraph, Semantic Kernel, Haystack, LlamaIndex, AutoGen.
• Strong understanding of:
  • Embeddings & vector search
  • RAG pipelines
  • Retrieval optimization
  • Chunking strategies
  • Document loaders & indexing
• Experience building AI apps using OpenAI SDK or similar.
• Experience deploying APIs/services using Docker and cloud environments.
• Leadership experience: guiding teams, conducting reviews, driving architecture decisions