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, selectors/filters, summarisation & compaction, token budgeting | Good to have |
| Agentic AI Development | Multi-agent design, tool calling, structured output, verification patterns | Good to have |
| 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
- 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. DeepLearning.AI, 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.
