Description
AI Engineering Intern
Location: Bengaluru, India
Department: Engineering.
About Eka.Care:
We’re building the Healthcare OS for India – a GenAI-powered platform trusted by 16M+ patients, 60K+ doctors, and health tech innovators. From smart clinics to large hospitals to patient-first apps, Eka Care is simplifying healthcare through design, data, and intelligence.
About the Role
We're looking for a curious, hands-on AI Engineering Intern who lives and breathes agentic systems. You won't just read about LLMs — you'll be building and optimising pipelines end to end. If you've stayed up late fine-tuning a prompt, wired up a RAG pipeline, or debugged why your agent keeps looping — this role is for you.
What You'll Do
Agent Development
Design and build multi-step agentic pipelines using frameworks like LangGraph, CrewAI, AutoGen, or custom orchestration logic
Implement tool-calling, memory management, and planning loops for autonomous agents
Debug and trace agent reasoning — understand why an agent took a decision, not just what it did
RAG Pipeline Engineering
Build end-to-end Retrieval-Augmented Generation (RAG) pipelines — strategies, models, stores
Understanding different RAG approaches
Profile and improve retrieval quality using recall/precision metrics
Evals & Experimentation
Understand, build and run LLM evaluation frameworks — both automated and human-in-the-loop
Track experiments rigorously — version prompts, log outputs, measure regressions
Publish findings internally as structured reports, notebooks, or dashboards
Prompt Engineering & Optimisation
Craft, iterate, and systematically optimise prompts for accuracy, consistency, and cost
Apply different prompting techniques
Use tools to automate prompt optimisation loops
Data & Publishing
Instrument pipelines to collect, clean, and analyse experiment data
Build lightweight dashboards or notebooks to communicate results to the team
Document learnings clearly — write-ups that others can learn from and reproduce
Must-Have Skills
Solid Python — you're comfortable with async, decorators, and debugging tracebacks at 1am
Hands-on experience building with LLM APIs (OpenAI, Anthropic, Gemini, or open-source models via Ollama/vLLM)
Working knowledge of vector databases and embedding pipelines
Familiarity with at least one agent framework (LangChain, LangGraph, CrewAI, AutoGen, or similar)
Understanding of eval methodologies — you know the difference between a good eval and a misleading one
Good to Have
Prior published work — a GitHub repo, blog post, or paper is a big plus
Experience with DSPy or other prompt optimisation frameworks
Exposure to observability tooling
Comfort with Jupyter notebooks and experiment tracking
Familiarity with cloud infra basics — deploying a FastAPI endpoint
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