Python AI / ML Engineer
Build AI features that ship: LLM integrations, retrieval pipelines, evaluation harnesses, and the data plumbing underneath. Production Python engineering first, model tinkering second.
What you'd be doing here
Clients increasingly ask us for AI features that actually work in production: document intelligence, retrieval-based assistants, classification pipelines, and workflow automation built on LLMs. We're hiring a Python engineer to build them. The emphasis is on the engineering: prompt and context design backed by evaluation, retrieval pipelines with measurable quality, and services that degrade gracefully when a model or provider misbehaves.
You'll work inside delivery squads next to backend and frontend engineers, taking features from a client's rough idea through prototype, evaluation, and deployment. Expect to spend as much time on data preparation, evals, and observability as on the model-facing code itself, because that's where these systems succeed or fail.
This is a Lahore office role. AI feature work involves fast, messy iteration with delivery leads and clients, and we've found it goes much better in the same room. You'll also help set our internal standards for how we build and evaluate AI features, since this practice area is growing quickly.
What you'll own
Design and build LLM-powered features: retrieval pipelines, structured extraction, classification, and agent-style workflows.
Write evaluation harnesses so quality is measured, tracked, and defended with numbers rather than vibes.
Build the Python services and APIs that expose AI features to product applications.
Own data preparation: cleaning, chunking, embedding, and keeping indexes fresh as source data changes.
Instrument AI features with logging, tracing, and cost tracking in production.
Prototype quickly with clients, then harden the winners into reliable, tested services.
Evaluate model and provider options against cost, latency, and quality for each use case.
Document patterns and mentor other engineers picking up AI feature work.
What we're looking for
3+ years of professional Python development with production code, tests, and reviews.
Hands-on experience building LLM-based features beyond tutorials: RAG, structured outputs, or tool use.
Understanding of embeddings, vector search, and the trade-offs in retrieval design.
Experience building and consuming APIs with FastAPI, Flask, or similar.
Comfort with SQL and practical data wrangling with pandas or similar tools.
The discipline to measure quality with evals instead of eyeballing outputs.
Bonus points, not blockers
If some of these describe you, mention it. If none do, apply anyway.
Classical ML experience with scikit-learn, XGBoost, or model fine-tuning.
Experience with vector databases such as pgvector, Pinecone, or Qdrant.
Familiarity with orchestration and observability tools for LLM apps.
Published projects, papers, or writing about applied AI work.
Tools you'll work with
Four steps, no surprises
We designed the process to respect your time and show you ours: every step mirrors how we actually work.
Application review
Apply through the form on this page. We read every application ourselves and reply within a week, including when the answer is no.
Technical interview
A conversation with senior engineers about systems you have built, decisions you made, and trade-offs you would make differently. No trick puzzles.
Practical exercise
A small, time-boxed task close to real project work, reviewed together like a normal code review. We evaluate how you think, communicate, and take feedback.
Culture conversation & offer
A final discussion with a delivery lead about how you like to work, followed by a clear written offer. The whole process typically takes two to three weeks.
Apply for Python AI / ML Engineer
Fill in the form below and we'll get back to you within a week, whatever the answer.
Prefer email? Send your CV to info@rndsol.com with the role in the subject line.