Senior AI Engineer

Unosecur; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Unosecur is seeking a senior AI architect to own the GenAI stack for a security SaaS product in Bengaluru. You will architect and ship LLM-driven features used in production, serving as the team’s AI voice and making architecture calls.

You’ll design RAG pipelines, multi-agent systems, and backend services in Python/FastAPI, with a strong emphasis on evaluation, observability, and enterprise-grade reliability. This role requires leadership and a hands-on, take-charge attitude.

Qualifications

• 10+ years of total engineering experience.
• 3+ years of hands-on GenAI/LLM work in production.
• Proficient in Python with FastAPI or Flask.
• Experience with LangChain/LangGraph in production.
• Implemented a RAG pipeline with a vector DB (e.g., Pinecone, Qdrant, Milvus, FAISS, PGVector, or ChromaDB).
• Experience at an enterprise SaaS product company.
• Based in Bengaluru or willing to relocate for 5 days a week in the office.
• Actively looking for a change.

Responsibilities

• Design, build and ship RAG pipelines and multi-agent systems in production.
• Build Python/FastAPI backend services that serve LLM features to enterprise customers.
• Build and integrate MCP servers and tool-calling workflows.
• Set up evaluation and observability for LLM outputs, with eval running in CI/CD.
• Collaborate with founders and take ownership with minimal spec.

Skills

Python

FastAPI/Flask

LangChain

LangGraph

RAG pipelines

Vector DB

Pinecone

Qdrant

Milvus

FAISS

PGVector

ChromaDB

Enterprise SaaS

10+ years

GenAI/LLM in production

Tools

FastAPI

Flask

LangChain

LangGraph

Pinecone

Qdrant

Milvus

FAISS

PGVector

ChromaDB

Job description

You'll be the technical owner of GenAI and agentic AI for a security SaaS product. You'll build the LLM-backed features customers use in production, not internal tools or demos. As the first and only AI voice on the team, you'll make the architecture calls yourself.

Responsibilities

• Design, build and ship RAG pipelines and multi-agent systems in production.
• Build Python/FastAPI backend services that serve LLM features to enterprise customers.
• Build and integrate MCP servers and tool-calling workflows.
• Set up evaluation and observability for LLM outputs, with eval running in CI/CD.
• Work directly with the founders and take ownership with minimal spec.

Requirements

• Python with FastAPI or Flask as your primary backend stack.
• Hands‑on experience with LangChain, LangGraph or a similar agent framework in real project work.
• A RAG pipeline you personally built, with a vector DB (Pinecone, Qdrant, Milvus, FAISS, PGVector or ChromaDB).
• 10+ years of total engineering experience.
• 3+ years of hands‑on GenAI/LLM work in production.
• Experience at an enterprise SaaS product company (not services or consulting).
• Based in Bengaluru, or willing to relocate for 5 days a week in the office.
• Actively looking for a change.

Good to have

• MCP built or integrated in production.
• RAGAS, DeepEval or LangSmith for evaluation.
• Security, identity or compliance domain exposure.
• Early‑stage startup or 0-to-1 build experience.
• AWS Bedrock or Google Vertex AI.
• Multi‑agent frameworks (AutoGen, CrewAI, Semantic Kernel, ADK).