What is the difference between an AI engineer and a data scientist?

A data scientist answers a question with data; an AI engineer ships a system that keeps answering it after you stop watching. Overlapping skills, different deliverable — most teams building a product need the second and hire the first.

Can they work with our existing models?

Yes. A large share of this work is inheriting something that already half-works — a prototype, a vendor integration, a fine-tune nobody can reproduce — and getting it to production standard.

Who owns the code they write?

You do, from the first commit. Work is done in your repository under your licence, and the contract assigns IP to you outright. We keep no rights over what we build for you and no dependency that makes leaving expensive.

What if it is not working out?

Two weeks written notice, no penalty, no minimum term beyond the first month. An engagement that needs a contract to hold it together has already failed — we would rather you could leave easily and chose not to.

STAFF AUGMENTATION

Hire AI Engineers

Ship AI features that survive production traffic, not demos.

2 weeks

notice period, no minimum term

What an AI engineer does here

In short: An AI engineer at Midalaxy owns the path from "the model works in a notebook" to "the feature holds up on a Monday morning". That is mostly not modelling. It is retrieval design, evaluation harnesses, latency budgets, failure paths and the decision about what the system must refuse to do.

The distinction matters when hiring. A researcher optimises a benchmark; an AI engineer optimises the thing your customer experiences, which is usually bounded by p99 latency and by how the system behaves on the inputs nobody anticipated.

What they are good at

LLM orchestration and tool calling
Retrieval design — chunking, hybrid search, re-ranking
Evaluation harnesses and regression sets for non-deterministic output
Prompt and context engineering under token budgets
Guardrails, refusal behaviour and PII handling
Latency profiling across model, network and retrieval
Cost control — caching, batching, quantisation, model routing
Production monitoring for silent quality drift

What they build

RAG systems

Grounded answering over your documents with citations, refusal behaviour and an evaluation set that catches regressions before customers do.

Agentic workflows

Multi-step agents with scoped tool access, explicit stop conditions and an audit trail of every decision taken.

Model serving

Inference endpoints sized for real concurrency, with fallback routing when a provider degrades.

The stack

Python
LangGraph / LangChain
OpenAI, Anthropic and open-weight models
pgvector, Pinecone, Qdrant
FastAPI
Docker and Kubernetes
Weights & Biases

Proof

CASE STUDY

SiteChat & Estimate256: RAG bots and ML estimation in production

Zero-code website chatbot platform plus a domain-tuned ML estimation engine — two production systems on one modern stack.

Any URL to chatbot, zero code3 domain-tuned XGBoost models

How you can engage them

Dedicated

Full-time on your product, in your standups and your repository. The right shape when the work is continuous and context compounds.

Extended team

Part-time capacity alongside your own engineers, for a specific workstream or a gap you are hiring against.

Project

A defined scope with an agreed outcome and end date. Best where the requirement is clear and unlikely to move.

For example, a client adding one engineer to an existing team interviews them, starts with the two-week trial, and decides after it — with nothing further to pay if they stop.

We do not publish rates, because the honest number depends on seniority, duration and notice. Tell us the budget you are working with and we will tell you what it buys — or say plainly if it does not buy enough.

How it actually starts

Scoping call

48 hours

What you are building, which skills it needs, and whether we are the right answer. Sometimes we are not, and you will hear that.

Profiles

3–5 days

CVs and code samples of the specific people available, not a generic capability deck.

You interview

your process

Technical interview with the actual engineer. Reject anyone you are unsure about — that costs nothing.

Trial period

first 2 weeks

Real work in your repository. If it is wrong, you stop, and there is nothing further to pay.

Onboarded

week 3

Shipping in your process, with a written record of decisions from the first day rather than the last.

Working across time zones

We are in Bangalore. That is an advantage for some of your working day and a constraint for the rest, and we would rather state the number than imply there is no gap:

London

4 hours of overlapping working day

Dubai

8 hours of overlapping working day

Singapore

7 hours of overlapping working day

New York

No natural overlap — async handover before your morning

San Francisco

No natural overlap — async handover before your morning

Sydney

4 hours of overlapping working day

Contract, ownership and exit

You own everything

IP assigns to you from the first commit. Work happens in your repository under your licence.

Two weeks notice

No minimum term past the first month, no exit fee, no penalty clause.

No lock-in by design

Standard tooling, documented decisions, no proprietary layer that makes leaving expensive.

Confidentiality

NDA before the scoping call if you want one, and we will sign yours rather than insisting on ours.

Frequently asked questions

What is the difference between an AI engineer and a data scientist?
A data scientist answers a question with data; an AI engineer ships a system that keeps answering it after you stop watching. Overlapping skills, different deliverable — most teams building a product need the second and hire the first.
Can they work with our existing models?
Yes. A large share of this work is inheriting something that already half-works — a prototype, a vendor integration, a fine-tune nobody can reproduce — and getting it to production standard.
Who owns the code they write?
You do, from the first commit. Work is done in your repository under your licence, and the contract assigns IP to you outright. We keep no rights over what we build for you and no dependency that makes leaving expensive.
What if it is not working out?
Two weeks written notice, no penalty, no minimum term beyond the first month. An engagement that needs a contract to hold it together has already failed — we would rather you could leave easily and chose not to.