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Posted 18 Aug 2026

SB-1486-AI Engineer Intern

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Employment
Full time
Team
Software Development
Posted
18 Aug 2026

About the role

AI Engineer InternRole SummaryWe 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-solvingMustProgrammingPython 3.10+ (async, typing); clean, idiomatic codeMustAgent OrchestrationLangGraph — graphs/state machines, checkpointers, HITL interruptsGood to haveContext EngineeringLayered context, selectors/filters, summarisation & compaction, token budgetingGood to haveAgentic AI DevelopmentMulti-agent design, tool calling, structured output, verification patternsGood to haveLLM IntegrationAnthropic & OpenAI / Azure OpenAI SDKs, prompt engineeringPreferredData ModellingPydantic v2, JSON Schema / typed contractsPreferredRetrievalVector stores (e.g. Qdrant / Azure AI Search), embeddingsPreferredContext ProtocolModel Context Protocol (MCP) — resources/tools, Streamable HTTPPreferredMulti-agent FrameworksCrewAI, Microsoft Agent FrameworkPreferredDurable WorkflowsTemporal (long-running, resumable flows)PreferredInferencevLLM awareness (paged attention, batching, quantisation), model routingPreferredQualifications & Certifications
  • Pursuing or recently completed B.Tech / B.E. / M.Tech / MCA in Computer Science or a related field from a reputable institution (or equivalent).
  • Final-year students and recent graduates welcome; strong fundamentals matter more than years of experience.
  • 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 any exposure to LLM / agentic AI through academic projects or self-learning — with clear eagerness to go deep on LangGraph and context engineering.
Preferred Certifications
  • Any recognised AI/ML or agentic-AI online course or certification (e.g. , Anthropic, Microsoft Azure AI Fundamentals).
  • Any cloud fundamentals 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 your approach to technical and non-technical people.
  • Eagerness to learn, high coachability, and the ability to take and act on feedback.
  • Collaborative team player who contributes to shared standards, code reviews, and knowledge sharing.


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