SB-1486-AI Engineer Intern

AI Engineer InternRole Summary

We are looking for five AI Engineering Interns to learn and contribute to production-grade agentic AI systems alongside our engineers. This is a hands-on, mentored internship centred on multi-agent orchestration, context management, and large language model (LLM) integration. It is open to final-year students and recent graduates — what matters most is outstanding computer-science fundamentals, strong data structures and algorithms (DSA) skills, and hands-on ability with Python.

You will work under the guidance of senior engineers on agent workflows and the context architecture behind them, contributing to real features while building production-grade skills. The ideal intern has a strong academic record, sharp problem-solving ability, genuine enthusiasm for the agentic AI stack, and the drive to convert this internship into a full-time AI Engineer role.

Key ResponsibilitiesAgent Orchestration & Workflow

• Assist in designing and implementing multi-agent workflows using LangGraph on Python with Pydantic structured output, under the guidance of senior engineers.
• Help model processes as stateful, resumable graphs with branching, looping, retries, and checkpointing.
• Support implementation of safe pause/resume and human-in-the-loop (HITL) checkpoints.

Context Engineering

• Learn and contribute to context management — layered context, retrieval/indexing, and active working sets.
• Help implement context selectors and filters, token-budgeted prompts, and summarisation/compaction of long histories.
• Assist in designing typed context schemas so each agent step receives precise, high-signal context.

LLM Integration & Retrieval

• Integrate LLM providers (e.g. Anthropic, OpenAI / Azure OpenAI) using prompt engineering, tool calling, and structured output, with mentorship.
• Help wire in retrieval — vector search and embeddings — and code-intelligence techniques for working over large codebases.
• Contribute to model-routing experiments that balance task type, latency, and cost.

Quality, Evaluation & Governance

• Help build evaluation and error-analysis loops; learn to treat failures as feedback that improves reliability.
• Assist in implementing verification and validation patterns and deterministic gates for agent outputs.
• Help keep agent decisions and context observable, auditable, and reproducible.

Collaboration

• Work with platform/infrastructure engineers on deployment, inference, and persistence tasks.
• Participate in design reviews, code reviews, and Demo Friday — sharing your work, including failed experiments.

Required Technical SkillsDomainSkills & TechnologiesMust / PreferredCS Fundamentals & DSAData structures, algorithms, complexity analysis, strong problem-solvingPython 3.10+ (async, typing); clean, idiomatic codeLangGraph — graphs/state machines, checkpointers, HITL interruptsLayered context, selectors/filters, summarisation & compaction, token budgetingMulti-agent design, tool calling, structured output, verification patternsAnthropic & OpenAI / Azure OpenAI SDKs, prompt engineeringPreferredPydantic v2, JSON Schema / typed contractsPreferredVector stores (e.g. Qdrant / Azure AI Search), embeddingsPreferredModel Context Protocol (MCP) — resources/tools, Streamable HTTPPreferredCrewAI, Microsoft Agent FrameworkPreferredTemporal (long-running, resumable flows)PreferredvLLM awareness (paged attention, batching, quantisation), model routingPreferred