AI Engineering Intern: Applied ML & Agentic Systems

AI Engineering Intern: Applied ML & Agentic Systems
Position Overview
Company: Yodaplus Technologies
Location: Mumbai, India — On-site / Hybrid
Duration: 6 months, Full-time
Start Date: Immediate
Conversion: Pre-placement offer based on performance
About the Role
You'll work where models meet software.
Half the job is classic ML/DL: training, evaluating, and shipping models behind real product features. The other half is agentic engineering: building LLM-powered agents, tools, and evaluations that hold up outside a demo.
We want someone who can work across both areas and is particularly strong in at least one.
What You'll Work OnApplied ML / Deep Learning
● Train, fine-tune, and evaluate models across Indian languages and scripts.
● Work with transliteration and spelling variants.
● Build and curate datasets.
● Analyse model failures and identify opportunities for improvement.
● Deliver measurable gains in accuracy, latency, and reliability.
Agentic & GenAI Engineering
● Build agents that plan, call tools, and complete multi-step, long-horizon tasks.
● Develop MCP servers that expose internal systems and capabilities to agents.
● Build RAG pipelines that return structured and validated outputs.
● Experiment with different agent architectures, tools, and workflows.
Evaluation & Reliability
● Build evaluation harnesses using:
○ Golden datasets
○ LLM-as-judge evaluations calibrated against human labels
○ Regression test suites
● Ensure every significant model or prompt change is measured before it ships.
● Add tracing and monitor model cost, latency, and reliability.
● Build systems that make AI behaviour measurable and debuggable.
Engineering — Non-Negotiable
● Write production-quality Python that is typed, tested, and maintainable.
● Use Git and participate in code reviews through PRs.
● Build and consume REST APIs.
● Containerise applications using Docker.
● Serve models behind production APIs.
● Use AI coding assistants as part of your daily workflow.
● Critically review and validate AI-generated code rather than blindly accepting it.
Must-Have SkillsPython & Software Engineering
Clean, tested code; Git; debugging; REST APIs; basic Linux and Docker.
ML Fundamentals
Data splits and leakage, bias–variance, precision/recall/F1, and understanding why a metric can sometimes mislead.
Deep Learning
You've trained at least one model in PyTorch that goes beyond a tutorial. You understand CNNs and transformers well enough to explain how and why they work.
LLM Application Basics
Prompting, tool/function calling, structured outputs using JSON Schema, embeddings, and retrieval.
Proof of Work
At least one project on GitHub — a model or an agent — that you can walk us through line by line.
Good-to-Have SkillsAgent Frameworks
LangGraph, OpenAI Agents SDK, Claude Agent SDK, Google ADK, Microsoft Agent Framework, or Pydantic AI.
Deep knowledge of one is enough. We care more about whether you understand how to build the agent loop yourself.
Protocols
MCP — built or used a server — and A2A.
Computer Vision
Face detection and recognition, face embeddings, liveness detection, and anti-spoofing.
Indic NLP
Transliteration, fuzzy and phonetic matching, and multilingual embeddings.
Fine-tuning & Serving
LoRA/QLoRA, quantization, ONNX, and vLLM.
Evals & Observability
Evaluation frameworks, tracing, and prompt versioning.
AI Security
Prompt injection, PII handling, and guardrails.
This is particularly important because we work with KYC and financial data.
What You'll GetReal Ownership
Your work ships into products with live customers, not a sandbox.
Mentorship
Weekly 1:1 with a Tech Lead and code review on every PR.
Rare Breadth
Model-building and agent engineering on the same desk.
Perks
Stipend, PPO opportunity, certificate, hardware, and other applicable benefits.
Who We're Looking For
We're looking for someone who is curious, hands-on, and comfortable learning by building.
You don't need to know every framework listed above. We care more about strong fundamentals, evidence that you've built something yourself, and the ability to understand systems deeply enough to debug and improve them.
If you've trained a model, built an agent, shipped a meaningful AI project, or gone down a technical rabbit hole because you wanted to understand how something works — we'd like to hear from you.

Work arrangement
Yes