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.

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.