Junior Agentic AI Engineer

Commerce Pundit; Ahmedabad District; On-site; INR 180,000 - 280,000; Full time

Job summary

Commerce Pundit, an AI-forward technology and services company in India, seeks an Agentic AI Engineer to design, build, and ship production-grade multi-agent systems that run autonomously and deliver measurable business outcomes.

You will architect agent pipelines, build MCP servers, and manage RAG/LLM cost efficiency, working closely with real clients to own end-to-end solutions from design to production.

Qualifications

• Hands-on experience building production multi-agent systems using LangGraph, CrewAI, AutoGen or equivalent.
• Deep understanding of agent orchestration patterns: Supervisor, Planner-Executor, Critic-Generator, hierarchical delegation.
• Experience designing typed state schemas with reducers for parallel agent coordination.
• Understanding of human-in-the-loop workflows including interrupt gates and async approval flows.
• Experience debugging real production failures in multi-agent systems—loops, state corruption, silent failures.
• Experience building or consuming MCP servers and exposing APIs/databases as MCP tools.

Responsibilities

• Architect agent pipelines and MCP servers for production environments.
• Design RAG systems, manage LLM costs at scale, and iterate with real users and clients.
• Own end-to-end production deployment of agentic systems from design to production.
• Collaborate with cross-functional teams to deliver measurable business outcomes.

Skills

LangGraph

CrewAI

AutoGen

Agent orchestration

RAG pipelines

Python

FastAPI

LangSmith

LangFuse

Tools

AWS

GCP

Docker

GitHub Actions

MCP servers

Pydantic

OpenAI API

Anthropic Claude

AWS Bedrock

Job description

We are looking for an Agentic AI Engineer who can design, build, and ship production-grade multi-agent systems — not demos,
not POCs, but real systems that run autonomously and deliver measurable business outcomes.

Commerce Pundit is a fast-growing technology and services company helping global e-commerce and enterprise clients build intelligent, AI-powered systems. We work across manufacturing, retail, beauty, auto parts, and digital commerce — solving real business problems with production-grade AI. We are a lean, high-output team where engineers own problems end-to-end and ship systems that directly impact client revenue and operations.

The Role

We are looking for an Agentic AI Engineer who can design, build, and ship production-grade multi-agent systems — not demos, not POCs, but real systems that run autonomously and deliver measurable business outcomes.

This is a hands-on IC role. You will be in the code daily — architecting agent pipelines, building MCP servers, designing RAG systems, managing LLM costs at scale, and iterating with real users and clients. If you are looking for a role where you can lead a large team without building yourself, this is not it. If you want to own a full agentic system from design to production, this is exactly it.

What We Are Looking For

Core Requirements

Multi-Agent Systems — Must Have

• Hands-on experience building production multi-agent systems using LangGraph, CrewAI, AutoGen, or equivalent
• Deep understanding of agent orchestration patterns — Supervisor, Planner-Executor, Critic-Generator, and hierarchical delegation
• Experience designing typed state schemas with reducers for parallel agent coordination
• Understanding of human-in-the-loop workflows including interrupt gates and async approval flows
• Experience debugging real production failures in multi-agent systems — loops, state corruption, silent failures
• Experience building or consuming MCP servers
• Understanding of MCP tool schema design, transport mechanisms (stdio / SSE), and security patterns
• Ability to expose existing APIs, databases, or scripts as structured MCP tools for LLM consumption

RAG & Retrieval — Must Have

• Hands-on experience building production RAG pipelines including document ingestion, chunking, embedding, and retrieval
• Understanding of hybrid search (BM25 + vector), reranking (cross-encoder or Cohere), and metadata filtering
• Experience with vector databases — Pinecone, Weaviate, Qdrant, ChromaDB, or pgvector
• Familiarity with RAG evaluation frameworks — RAGAS, TruLens, or DeepEval
• Strong prompt engineering skills including system prompt design, structured output enforcement, and few-shot examples
• Understanding of token cost optimization — prompt caching, model routing, semantic caching, token budget management
• Experience with LLM APIs — OpenAI, Anthropic Claude, AWS Bedrock, Google Gemini
• Ability to design and implement evaluation pipelines for LLM output quality

Production Engineering — Must Have

• Python proficiency — FastAPI, async patterns, Pydantic, background workers
• Experience deploying AI systems to production with proper observability — LangSmith, LangFuse, or equivalent
• Understanding of production failure modes — silent failures, hallucination detection, retry logic, circuit breakers
• Experience with cost tracking and monitoring for LLM API spend at production scale

Infrastructure & DevOps — Good to Have

• AWS or GCP experience — Lambda, ECS, S3, or equivalent
• Docker and basic containerization
• CI/CD pipeline familiarity — GitHub Actions, Jenkins, or equivalent

Technical Stack You Will Work With

• Orchestration: LangGraph, CrewAI, AutoGen
• LLM APIs: Anthropic Claude, OpenAI GPT, AWS Bedrock, Google Gemini
• Cloud: AWS (primary), GCP
• Observability: LangSmith, LangFuse, CloudWatch