Junior Agentic AI Engineer

Job Description About Commerce Pundit

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

MCP (Model Context Protocol) - Strong Plus

• 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

LLM Engineering - Must Have

• 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
• RAG: LangChain, LlamaIndex, hybrid search, cross-encoder reranking
• Vector Databases: Pinecone, Qdrant, ChromaDB, pgvector
• MCP: Custom server development, tool schema design
• Backend: Python, FastAPI, Node.js
• Cloud: AWS (primary), GCP
• Observability: LangSmith, LangFuse, CloudWatch
• Evaluation: RAGAS, custom eval pipelines