ML Engineer I

Role Overview

We are looking for an AI Engineer with strong experience in Retrieval-Augmented Generation (RAG) to design, build, and operate scalable GenAI backend systems. The role focuses on Python backend development, agentic AI workflows, vector search, and production-grade LLM pipelines.

Responsibilities

• Design, develop, and maintain Python backend services using FastAPI and/or Flask, following clean architecture and best practices.
• Build and expose REST APIs for GenAI capabilities including agents, retrieval, orchestration, evaluation, and observability.
• Implement Agentic AI workflows using LangChain and LangGraph, including tool calling, planning, multi-step execution, and state graphs.
• Develop end-to-end RAG systems: data ingestion, chunking, embeddings, retrieval, reranking, and response grounding.
• Integrate and optimize vector databases such as FAISS, Pinecone, and Weaviate for semantic search and retrieval.
• Work with structured data sources and warehouses including MySQL, PostgreSQL, and Snowflake.
• Collaborate with product, data, and infrastructure teams to translate requirements into reliable, scalable AI systems.
• Write unit and integration tests, enforce quality checks through GitHub CI/CD, and ensure production readiness.

Requirements

• Strong experience in Retrieval-Augmented Generation (RAG).
• Proficiency in Python backend development.
• Experience with agentic AI workflows and vector search.