AI Engineer Intern

Job Description & Official Details

Job Description

Engineering · Internship · India · remote-friendly · ₹5,000–15,000 a month

AI Engineer Intern

Build the decision and reasoning layers that turn a sampled population into a prediction — and make every model in the loop replaceable, measurable and honest.

🎯 What Success Looks Like in This Role

Integrate decision models (Jev, Claude, open models) behind one contract and benchmark them against each other

Harden the simulation pipeline on Cloudflare Workers, Workflows and D1: retries, validation, provenance, cost

Build guards that keep language models from inventing numbers

Instrument runs so every prediction can later be scored against reality

You might be a fit if

You have shipped something with an LLM and watched it fail in a way you didn't expect

You are comfortable in TypeScript or Python and curious about the other

You read model outputs as data to be validated, not answers to be trusted

What you'll learn

Production LLM systems end to end

Evaluation and calibration in practice

Edge infrastructure at low cost

🎯 Candidate Preparation Notes & Expectations

Candidate Preparation Notes for AI Engineer Intern at Thommidhi

As a fresher looking to land the AI Engineer Intern role at Thommidhi, it's essential to focus on developing skills that align with the job requirements. Here are some practical tips to help you prepare:

Technical Skills to Brush Up On

• •TypeScript and Python programming languages
• •Cloudflare Workers and Workflows
• •Familiarity with D1 and LLM (Large Language Models)
• •Understanding of model integration, benchmarking, and evaluation

Relevant Project/Portfolio Practice

• •Develop a personal project that involves integrating decision models with language models
• •Experiment with Cloudflare Workers and Workflows to build a simulation pipeline
• •Practice building guards to prevent language models from inventing numbers
• •Instrument runs to score predictions against reality

Problem-Solving Readiness

• •Develop a critical thinking mindset to evaluate and calibrate model outputs
• •Practice reading model outputs as data to be validated, not answers to be trusted
• •Learn to approach problems with a production-ready mindset, considering edge infrastructure and cost

Realistic Role Expectations

• •Be prepared to work on integrating decision models with language models
• •Collaborate with the team to harden the simulation pipeline and instrument runs
• •Develop a growth mindset to learn from failures and improve model performance
• •Stay curious and open to learning about production LLM systems, evaluation, and calibration in practice.