Hire AI Developers and Machine Learning Engineers

Hire AI developers who can take a use case from a promising demo to something your staff rely on, with the evaluation, cost control and governance that production requires. PS3G’s AI engineers build language-model applications, retrieval pipelines, agents and classic machine learning on OpenAI, Azure OpenAI, Gemini and open models, and they know which of those a given problem needs. They join your team as named individuals or as a dedicated team, for organizations of every size: a founder adding an AI feature to a product, an operations team automating document-heavy work, or a department inside a larger organization with a mandate to ship something real this year.

Hire AI developers and machine learning engineers who are managed, not just placed

The AI engineers you hire through PS3G come from our AI development practice, where we build AI features into our own products as well as our clients’. They are PS3G employees, screened by our senior AI and backend engineers and supported by a technical lead and an account manager throughout. That matters more in AI than in most fields: the tools change monthly, and an engineer with senior colleagues to consult on a retrieval design or an evaluation method makes better decisions than one learning alone. We are an engineering firm, not a staffing agency.

AI work is integration work. Our engineers build in Python and TypeScript, connect to .NET, Node.js and Java systems, read from your databases and document stores, and work with the AI governance controls your organization needs.

What they can do for you

AI developers for hire through PS3G are matched to the kind of work below, which differs more than the job title suggests.

• LLM applications. Assistants, document drafting, summarization, classification and extraction features built on model APIs, with prompt management, structured outputs and fallbacks.
• Retrieval-augmented generation. Chunking, embedding and indexing your documents, hybrid search, reranking, citation and the evaluation set that tells you whether answers are getting better. Our guide to RAG vs fine-tuning explains when each applies.
• Agents and workflow automation. Tool-using agents that take actions in your systems with permissions, approvals, logging and limits designed in.
• Classic machine learning. Forecasting, scoring, anomaly detection and recommendation models in scikit-learn, XGBoost or PyTorch, where a language model is the wrong tool.
• MLOps and model serving. Pipelines for training and evaluation, model registries, serving endpoints, monitoring for drift and cost, and the retraining process.

Seniority levels

A mid-level AI developer implements features against a defined design: prompts, retrieval steps, API integrations and tests, following the evaluation and logging conventions already in place. A senior engineer designs the approach (which model, retrieval or fine-tuning, agent or workflow), builds the evaluation set first, sets the cost and latency budget, and reviews the team’s work. A lead engineer owns the AI platform across use cases, the vendor and model decisions, the governance interface and the conversation with your business owners.

How hiring works

1. Brief. A first conversation on the use case, the data and documents involved, the systems the feature must live in, the model providers you can use, your governance requirements, and the reporting line and hours you need.
2. Vetted profiles in days. A short list of AI engineers with the applications or models they have shipped, the providers and frameworks they have used, their evaluation experience and their availability.
3. Interviews. Your team interviews each candidate. We suggest a session on a small, real problem: a sample of your documents and a question set, with the candidate explaining how they would build and measure an answer.
4. Start. Access to repositories, model accounts, data and the target systems, an evaluation set drafted in the first days, and first tickets that produce a measured baseline within the first week or two.
5. Scale monthly. Add an engineer when the second use case starts, reduce to maintenance once the feature is stable, or convert an individual into a dedicated AI team, with notice.

Describe the use case and we will send AI engineers who have built something like it.

Vetting: a technical screen by our own engineers

Senior PS3G engineers screen every AI developer on a working problem rather than a vocabulary test. The candidate is given a small document set and a question set and asked to design a retrieval pipeline, say how they would evaluate it, and estimate its cost per query. They review code with planted problems (prompt injection through retrieved content, a tool call with no permission check, personal data sent to a model without a contract that allows it, an evaluation that only tests happy paths, retries without idempotency, no logging of inputs and outputs) and explain each. Machine learning candidates work through a modeling problem instead: data leakage, validation design and metric choice.

The pitfalls when you hire LLM developers and machine learning engineers are new and common. Many candidates have built a chat demo over a vector store and never measured whether it answers correctly. Some confuse model fluency with correctness and skip evaluation entirely. Data scientists are sometimes hired as AI engineers and cannot ship a service, or the reverse. Cost and latency are often discovered after launch. And few candidates think about governance, logging and access until a compliance team asks. Our screen covers all of these before you see a profile.

Individual developers vs a dedicated team vs a fixed-scope project

You can hire AI engineers individually into your team, engage a dedicated AI team led by PS3G, or hand us a defined use case to deliver as a project.

Option	Who manages	Fits when	Example
Individual AI developers	Your technical lead	You have a product team and a process, and need AI-specific experience inside it	A senior AI engineer joining your product team to build and evaluate a document-extraction feature
Dedicated AI team	A PS3G lead, against your priorities	You have several use cases and want a team that builds, evaluates and runs them to one standard	Two AI engineers, a data engineer and a lead building a retrieval assistant over your knowledge base and an agent for a back-office workflow
Fixed-scope project	PS3G owns delivery	The use case is defined and you want a measured result at a fixed price	A proof of concept with an evaluation set and a production plan, as described in our guide to AI proofs of concept

Individuals and teams run monthly under our staff augmentation terms and scale with notice. A project is quoted as a whole, and for AI we recommend the proof-of-concept shape with an evaluation set as the deliverable. Running and monitoring models afterwards is a managed service under a defined SLA, and an AI readiness assessment or governance setup is advisory. We start with a first conversation, a short assessment and a written proposal that prices roles monthly by seniority and location; hourly rates are not published.

Related services

• Hire developers and dedicated teams: every technology we staff.
• AI development: the practice our AI engineers come from.
• AI governance: policies, risk assessment and controls for the systems these engineers build.
• IT staff augmentation: monthly terms, replacements and IP ownership.
• IT staffing: data engineers and analysts alongside your AI developers.

Frequently asked questions

What is the difference between hiring an AI developer and hiring a machine learning engineer?

An AI developer builds applications on top of models: prompts, retrieval, agents, integrations and evaluation. A machine learning engineer builds and operates models: data preparation, training, validation, serving and monitoring. Most business use cases today need the first; forecasting, scoring and detection problems need the second. We ask which in the brief and staff accordingly.

Which model providers do your AI developers work with?

OpenAI, Azure OpenAI, Google Gemini and open models run on your own infrastructure or through a hosting provider. We recommend by use case, data-handling requirements and cost, and we build so that switching models later is a configuration change rather than a rewrite.

Can you hire generative AI developers who also handle governance?

Our engineers build with logging, access control, data handling and evaluation from the start, and they work with your governance process or ours. For policies, risk assessments and alignment with frameworks such as the NIST AI RMF, our AI governance consultants work alongside the engineers rather than replacing them.

Can I hire data scientists through PS3G?

Yes, for analysis, modeling and experimentation roles. In the brief we distinguish between a data scientist who explores and models, a machine learning engineer who productionizes, and an AI developer who builds applications, because hiring the wrong one is the most common mistake we see.

Where are your AI developers located?

AI engineers are based in the US and at our India delivery center, with account management at our headquarters in Wilmington, Delaware. Where data-handling rules restrict who can access certain data, we staff accordingly and say so in the proposal. India-based engineers keep hours that overlap the US day and attend your stand-ups live.