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AWSWhy hire AI/ML Developers through us
- Vetted engineers — technical screening and a practical exercise before you see a profile
- You interview and choose; nobody joins without your approval
- Working hours overlap with your team, with clear daily communication
- NDA and full IP assignment to your company
- Monthly terms with a trial period and free replacement if the fit is wrong
Roles and seniority
| Level | What they do |
|---|---|
| ML engineer | Builds and deploys models and data pipelines |
| LLM application engineer | Builds assistants, RAG and agent workflows |
| AI lead | Designs AI architecture, evaluation and governance |
Skills matrix
Ways to engage
| Model | How it works | Best for |
|---|---|---|
| Full-time dedicated | One engineer, 40 hours a week on your work | Ongoing product development |
| Part-time | 20 hours a week | Maintenance and smaller roadmaps |
| Team | Several engineers with a lead | New products and workstreams |
Rates depend on seniority and engagement length. Share your requirements and you will receive a written rate card within one business day. See also staff augmentation and dedicated teams.
What a great AI/ML developer looks like
Excellent AI engineers combine curiosity about models with discipline about engineering. They can prototype quickly, but they also build evaluation sets, measure accuracy, monitor cost and latency, and design systems that degrade gracefully when a model is wrong. For LLM work they understand retrieval, prompt design, tool use and safety; for classical machine learning they understand data preparation, validation and deployment. They explain capabilities and limitations honestly to business stakeholders.
Questions we ask in technical interviews
Every candidate is assessed on fundamentals and on judgement. Typical topics include:
- How would you evaluate the accuracy of a question-answering assistant?
- How do you reduce hallucinations in a RAG system?
- How would you control LLM costs at scale?
- How do you prevent an AI agent from taking unsafe actions?
- How do you monitor a model for drift in production?
Typical projects for AI/ML developers
- Building a support assistant grounded in your help centre and policies
- Automating document processing for invoices, forms and contracts
- Creating AI agents that update CRM records and draft responses
- Building demand forecasts or lead-scoring models
- Adding AI features to an existing SaaS product
For first projects, we usually recommend pairing an AI engineer with a product-minded lead who can define measurable success criteria.
LLM application engineer or machine learning engineer?
“AI developer” covers two quite different jobs. An LLM application engineer builds products on top of existing models: prompt and tool design, retrieval over your documents, evaluation and guardrails, cost control and integration into your software. A machine learning engineer trains and deploys models on your own data: feature pipelines, model selection, experiment tracking and monitoring for drift.
| LLM application engineer | Machine learning engineer | |
|---|---|---|
| Typical work | Chat assistants, document Q&A, agents, extraction | Forecasting, classification, recommendations, anomaly detection |
| Core skills | APIs, retrieval, evaluation, back-end engineering | Statistics, data pipelines, training and serving models |
| Data needed | Your documents and examples of good answers | Historical labelled data in reasonable volume |
| Main risks | Wrong answers, cost, data leakage | Poor data quality, drift, overfitting |
Many projects need a little of both. Be clear about which is the primary job before you hire.
Red flags when hiring AI/ML developers
- Talks about model choice but not about how results will be evaluated
- Has only built notebooks and demos, never a system used in production
- Has no plan for handling wrong or uncertain outputs
- Ignores data privacy, retention and access controls
- Cannot estimate the running cost of the solution they propose
How to scope a first AI engagement
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Pick one workflow
Choose a single, measurable task — for example drafting replies to a common type of support ticket.
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Collect examples
Gather real inputs and good outputs to build an evaluation set before writing code.
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Build a narrow version
Deliver a working version for a small group of users with logging and feedback.
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Measure and decide
Compare quality, time saved and cost against the evaluation set, then decide whether to expand.
Related services: generative AI development, RAG development and machine learning development.
Tools AI/ML developers typically use
Questions to answer before you hire
- What decision or task will the AI support, and who will use the output?
- What data is available, who owns it and can it leave your environment?
- How will you know the system is good enough — and what happens when it is wrong?
- Who will maintain prompts, models and evaluation sets after launch?
Clear answers to these questions often matter more than the choice of model, and they help a new developer make useful progress in the first weeks.
Working with your data and security teams
AI developers need access to data, which makes early agreement with your security team essential. Decide which data the developer may use, whether it can be sent to third-party model providers, how long logs and prompts are retained and who reviews outputs before they reach customers. Settling these points in week one prevents delays later and makes approval of the finished system much easier.
Agreeing these rules early also builds trust with the people who will use the system day to day.
Onboarding timeline
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RequestDay 1
You share the role, skills and start date.
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ProfilesWithin 48 hours
Shortlisted candidates with relevant experience.
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InterviewsDays 3–5
You interview and select.
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StartWeek 2
Access, onboarding and first tasks in your tools.
Prefer a managed project instead of hiring? See our services.
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