SB-1486-AI Engineer Intern

AI Engineer Intern Role 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 Responsibilities Agent 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 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 Good to have Context Engineering Layered context,…