AI Engineering Intern: Applied ML & Agentic Systems

Yodaplus Technologies Private Limited; India; Hybrid; INR 391,000 - 781,000; Full time

Benefits offered by this job

Stipend

PPO opportunity

Certificate

Hardware

Job summary

Yodaplus Technologies Private Limited in Mumbai invites you to join as an AI Engineering Intern focused on Applied ML and Agentic Systems. You will train and evaluate models, build datasets, and develop agents that plan and execute multi-step tasks in real products.

The role blends ML/DL with practical engineering, requires Python, PyTorch, REST APIs, and Docker, and a track record you can walk through in a GitHub project.

Qualifications

• Proficient Python, typed, tested code.
• Familiar with REST APIs, Git workflows and Docker.
• Experience with ML/DL model development and evaluation.
• Exposure to multilingual data, transliteration, and NLP basics.
• Portfolio with a model or agent project on GitHub.

Responsibilities

• Train, fine-tune, and evaluate models across languages.
• Build and curate datasets for multilingual settings.
• Analyse failures to improve model performance.
• Develop agents that plan, call tools and perform tasks.
• Expose models behind production APIs and ensure reliability.
• Establish tracing, cost monitoring, and observability.

Skills

Python

Software Engineering

REST APIs

Docker

Git

ML Fundamentals

Deep Learning

LLM Basics

Agent Frameworks

Protocols

Computer Vision

Indic NLP

Fine-tuning & Serving

Evals & Observability

AI Security

Job description

Company: Yodaplus Technologies

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 On

Applied 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 Skills

Python & 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 Skills

Agent 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 Get

Real 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.