AI Engineer
Role Summary
We are looking for AI Agent Architects to design and build production-grade agentic AI systems. This is a deeply hands-on engineering role centred on multi-agent orchestration, advanced context management, and large language model (LLM) integration, strong data structures and algorithms (DSA) skills, and hands-on ability with Python.
You will work on the design of agent workflows and the context architecture that makes them reliable, accurate, and efficient, taking systems from prototype to production. The ideal candidate has a strong academic record, sharp problem-solving ability, and genuine enthusiasm for going deep on the agentic AI stack.
Key Responsibilities
Agent Orchestration & Workflow
- 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.
Context Engineering
- 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.
LLM Integration & Retrieval
- 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.
Quality, Evaluation & Governance
- 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.
Collaboration
- Partner with platform/infrastructure engineers on deployment, inference, persistence, and durable execution.
- Contribute to engineering standards, design reviews, and code quality.
Required Technical Skills
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
Qualifications & Certifications
- 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.
Preferred Certifications
- Microsoft Certified: Azure AI Engineer Associate
- Any recognised cloud certification (Azure / AWS / GCP) is a plus
Soft Skills & Cultural Fit
- 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.
What We Offer
- 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.
