AI Engineer
Summary
Build production-grade agentic AI systems using LangGraph, Python, and LLM integration, focusing on multi-agent orchestration, context management, and durable workflows.
SB-1482-AI Engineer
Location: Kochi, India
Department: Software Development
Experience: 2-4 Years
- Design and implement multi-agent workflows using LangGraph on Python with Pydantic structured output.
- Model complex, long-running processes as stateful, resumable graphs with branching, looping, retries, and durable checkpointing.
- Implement safe pause/resume and human-in-the-loop (HITL) checkpoints.
- Engineer context management as a first-class subsystem — layered context, retrieval/indexing, and active working sets.
- Implement deterministic context selectors and filters, token-budgeted prompts, and summarisation/compaction of long histories.
- Design typed context schemas so each agent step receives precise, high-signal context.
- Integrate LLM providers (e.g. Anthropic, OpenAI / Azure OpenAI, and self-hosted models) using robust prompt engineering, tool calling, and structured output.
- Wire in retrieval — vector search and embeddings — and code-intelligence techniques for working over large codebases.
- Contribute to model-routing logic that balances task type, risk, latency, and cost.
- Build evaluation and error-analysis loops; treat failures as feedback that improves reliability over time.
- Implement verification and validation patterns and deterministic gates for agent outputs.
- Ensure agent decisions and context are observable, auditable, and reproducible.
- Partner with platform/infrastructure engineers on deployment, inference, persistence, and durable execution.
- Contribute to engineering standards, design reviews, and code quality.
| Domain | Skills & Technologies | Must / Preferred |
| CS Fundamentals & DSA | Data structures, algorithms, complexity analysis, strong problem-solving | Must |
| Programming | Python 3.10+ (async, typing); clean, idiomatic code | Must |
| Agent Orchestration | LangGraph — graphs/state machines, checkpointers, HITL interrupts | Must |
| Context Engineering | Layered context, selectors/filters, summarisation & compaction, token budgeting | Must |
| Agentic AI Development | Multi-agent design, tool calling, structured output, verification patterns | Must |
| LLM Integration | Anthropic & OpenAI / Azure OpenAI SDKs, prompt engineering | Preferred |
| Data Modelling | Pydantic v2, JSON Schema / typed contracts | Preferred |
| Retrieval | Vector stores (e.g. Qdrant / Azure AI Search), embeddings | Preferred |
| Context Protocol | Model Context Protocol (MCP) — resources/tools, Streamable HTTP | Preferred |
| Multi-agent Frameworks | CrewAI, Microsoft Agent Framework | Preferred |
| Durable Workflows | Temporal (long-running, resumable flows) | Preferred |
| Inference | vLLM awareness (paged attention, batching, quantisation), model routing | Preferred |
- Strong academic record — B.Tech / B.E. / M.Tech / MCA in Computer Science or a related field from a reputable institution (or equivalent).
- Strong data structures, algorithms, and problem-solving skills — a competitive-programming track record (Codeforces / LeetCode / ICPC / similar) is a strong plus.
- Hands-on Python, plus exposure to LLM / agentic AI through academic projects, internships, or work — with clear eagerness to go deep on LangGraph and context engineering.
- Microsoft Certified: Azure AI Engineer Associate
- Any recognised cloud certification (Azure / AWS / GCP) is a plus
- Strong analytical mindset with a structured approach to design, debugging, and root-cause analysis.
- Clear written and verbal communication — able to explain agent and context design to technical and non-technical stakeholders.
- Comfortable with ambiguity and able to work independently with minimal supervision.
- Collaborative team player who contributes to shared standards, code reviews, and knowledge sharing.
- Deep, hands-on work with the modern agentic AI stack — LangGraph, MCP, and multi-agent systems.
- High ownership and influence over architecture from an early stage.
- Competitive compensation with a structured performance review process.
- Professional development support — certifications, conferences, and access to emerging tooling.
- Collaborative, transparent culture with clear growth pathways toward Staff / Principal engineering.