Staff augmentation & dedicated AI teams
Hire AI engineers who join your team, not a ticket queue
Senior AI/ML, LLM, RAG and MLOps engineers embedded in your sprints, on your repositories, your tools and your stand-ups, from a single specialist to a dedicated pod. Start in weeks rather than the months an in-house search takes, and scale up or down with notice.
In one paragraph
AIYatch staff augmentation places vetted, senior AI engineers directly inside your team. You brief us on the role, we shortlist from our own engineers within days, you interview and run a short trial, and the engineer works in your repositories and rituals under your direction. Everything they build is yours, covered by NDA and IP assignment; no recruitment fees, and you can scale, swap or end the engagement with notice.
Why augment with AIYatch
Why teams add AIYatch engineers instead of hiring or freelancing
Weeks to a working engineer, not months
An in-house AI hire typically takes a quarter or more of sourcing, interviewing and notice periods. We shortlist from people already on our team, so the gap between "we need this" and "first pull request" is measured in weeks.
Seniors who have shipped AI to production
The engineers we place are the same people who deliver our agent, RAG, computer-vision and MLOps projects. You see the actual person, their work and their references before you commit, with no substitutions after signing.
Your direction, our execution
Augmentation means you own the roadmap, the architecture decisions and the code review. Our engineers plug into your sprint cadence, your issue tracker and your definition of done. If you would rather we lead delivery, we say so and propose a project or pod instead.
Working hours that overlap with yours
From Lahore we overlap the full working day with Europe and the Middle East, the mornings with the US East Coast and afternoons with Australia. Asynchronous by default, live when it matters: reviews, planning, incidents.
NDA, IP assignment and least-privilege access
NDA before any profile is shared, IP assigned to you in the contract, work done in your accounts and repositories, and secrets kept out of model context and personal machines. Data-flow and access are documented before the engineer starts.
One engineer today, a pod next quarter
Start with a single embedded specialist and add data, MLOps or full-stack capacity as the work proves itself. Or reduce when the launch is done. Changes take effect with notice, not with renegotiation.
Roles
Senior specialists, ready to embed.
Every profile is hands-on and has shipped production AI. Mix and match into a pod.
AI / ML engineer
Designs, trains and evaluates models against a baseline you can reproduce (classification, forecasting, recommendation, anomaly detection) and gets them into a service your product can call. Related pillars: data-analytics-predictive-ai, ai-fine-tuning.
PythonPyTorchscikit-learnXGBoostfeature engineeringevaluation & experiment trackingFastAPI model servingLLM & agent engineer
Builds LLM-powered features and tool-using agents with guardrails, evaluation sets and human approval where actions are irreversible. Multi-model orchestration across OpenAI, Anthropic, Gemini and open-source models. Related pillars: ai-agents, generative-ai-llm.
OpenAI / Anthropic / Gemini APIsLangGraphfunction calling & MCPmulti-agent orchestrationprompt & eval designguardrailsTypeScript / PythonRAG & search engineer
Turns your documents and databases into a grounded, citable knowledge layer: chunking and embedding pipelines, hybrid search, re-ranking, permissions-aware retrieval and answer evaluation. Related pillar: rag-systems.
Pinecone / Weaviate / Qdrant / pgvectorembeddings & re-rankershybrid & semantic searchdocument parsing & OCRretrieval evaluationaccess-controlled indexingData scientist
Frames the business question, explores and cleans the data, builds and validates models, and explains the result to non-specialists, with the honesty to say when the data cannot support the claim. Related pillar: data-analytics-predictive-ai.
SQL & pandasstatistics & experimentationtime-series forecastingchurn & propensity modelsdashboards (Power BI / Looker / Metabase)model cards & reportingMLOps engineer
Owns the path from notebook to reliable service: CI/CD for models, GPU and inference optimisation, monitoring for drift, cost and latency, and the runbooks your platform team can operate. Related pillar: mlops-ai-infrastructure.
Docker & KubernetesAWS / GCP / AzureMLflow / Weights & BiasesvLLM / TGI inferenceobservability (Prometheus, Grafana, LangSmith)Terraformmodel & prompt CIComputer-vision engineer
Detection, segmentation, OCR and video analytics for defect detection, retail, safety and document workflows, including data-capture plans, labelling and edge deployment. Related pillar: computer-vision.
YOLO / Detectron / SAMOpenCVOCR pipelinesvideo analyticsannotation workflowsedge deployment (Jetson, ONNX, TensorRT)Full-stack AI developer
Ships the product around the model: APIs, integrations, streaming chat UIs, admin dashboards and the SaaS plumbing that turns a prototype into something customers pay for. Related pillars: ai-saas-product-development, ai-integration.
TypeScript / React / Next.jsNode or Python backendsPostgreSQLstreaming & webhooksCRM / ERP integrationsauth, billing & multi-tenancyAI product / solution architect
Chooses what to build and how: use-case prioritisation, architecture, model and vendor selection, cost and risk analysis, and the roadmap your engineers and ours execute against. Related pillar: ai-consulting-strategy.
AI opportunity mappingarchitecture & data-flow designbuild-vs-buy & vendor selectioncost / ROI modellingsecurity & governanceroadmap & backlog shaping
Engagement models
Choose the shape of the team.
Single embedded engineer
One senior specialist joins your team full-time or part-time, works in your repositories and rituals, and reports to your lead. The lightest way to close a specific gap: an LLM engineer for a new feature, an MLOps engineer for a launch.
Best for: A defined skill gap in an existing engineering team
Dedicated AI pod
A small cross-functional team (for example an LLM engineer, a data or MLOps engineer and a full-stack developer) with a named lead on our side. Runs inside your planning cycle and reports progress weekly.
Best for: A roadmap of AI work that needs sustained capacity
Project-based build
When you want an outcome rather than people: a scoped build with milestones, demos every one to two weeks and a clean handover. Fixed price or capped time-and-materials, following our discover–design–build–validate–operate process.
Best for: A well-defined system with a clear finish line
AI advisory / fractional AI lead
A senior architect or our founder acting as your part-time head of AI: prioritisation, architecture reviews, vendor decisions, hiring support and a monthly roadmap check. Often paired with one embedded engineer.
Best for: Teams that have developers but no AI leadership
How it works
From brief to embedded engineer in days, not months.
- 01
Brief
A short call or written brief: the problem, the stack, the level of seniority you need, hours of overlap and start date. We sign an NDA at this stage if you prefer.
- 02
Shortlist in days
We propose one to three engineers from our own team with real profiles, relevant work and availability, not a marketplace pool. If we do not have the right person, we say so rather than stretch.
- 03
Interview and trial
You interview the candidates and, if you wish, run a paid trial of a sprint or two on real work. If the fit is wrong, you stop there and owe nothing further.
- 04
Onboard
Access, accounts and repositories set up under least privilege; a short onboarding plan agreed with your lead; the engineer joins your stand-ups and ships in the first sprint.
- 05
Scale, swap or wind down
Monthly check-ins on fit and output. Add capacity, change the role or reduce with notice. If a placement stops working, we replace the engineer and manage the handover.
Compare
In-house hire vs freelancer vs AIYatch augmentation
| Criterion | In-house hire | Freelancer | AIYatch staff augmentation |
|---|---|---|---|
| Time to start | Typically a quarter or more: sourcing, interviews, notice periods | Days, if you find the right person yourself | Shortlist in days; engineer working within a few weeks |
| Cost structure | Salary plus benefits, equipment, recruitment and management overhead | Lowest hourly rate; high variance in quality and availability | One monthly or hourly rate that includes vetting, payroll and replacement; no recruitment fee |
| Seniority and vetting | As good as your hiring process for a scarce skill | Self-declared; you carry the vetting | Senior engineers who deliver our own AI projects; you interview them before committing |
| Continuity | High, until they leave, and AI talent is in demand | Low; individuals move on between projects | High; the same engineer stays, with a managed replacement if life happens |
| Management overhead | Full: HR, reviews, growth, tooling | High: you manage the individual and any gaps | Low: you direct the work, we handle the employment side and provide a point of escalation |
| IP and security | Fully under your control | Depends on the individual contract and setup | NDA, IP assignment, work in your accounts, least-privilege access documented |
| Scaling | Each additional head is a new hiring cycle | One person at a time; hard to add a coherent team | From one engineer to a pod, up or down with notice |
Guarantees
Terms that keep the risk on our side.
Trial before you commit
A paid trial period on real work (usually a sprint or two) before any longer engagement. If the fit is not right, you stop at the end of the trial with nothing further owed.
Replacement if it is not working
If an engineer is unavailable or the placement is not delivering, we replace them at no additional recruitment cost and manage the handover so your work does not stall.
NDA and IP assignment as standard
NDA available before any profile is shared. Everything built during the engagement is assigned to you and lives in your repositories and cloud accounts.
No recruitment or placement fee
You pay for the engineer's time. Sourcing, vetting, payroll and administration are included in the rate. If you want to hire an engineer permanently, the conversion terms are agreed upfront in your contract, so there are no surprises.
Cancel or change with notice
After the trial, engagements have a short minimum term (typically three months) and then run month to month. You can reduce, expand or end them with the notice period stated in your agreement, with no penalties for changing your mind as priorities move.
FAQ
Staff augmentation: questions we get
Usually within a few weeks of the brief. We shortlist from engineers already on our team, so you see profiles within days; the interview, trial agreement and access setup then set the pace. If we do not have the right person available, we tell you at the shortlist stage rather than stretch a poor fit.
With augmentation you direct the work: our engineer joins your team and follows your roadmap, code review and rituals. With a project we own delivery against a scope, milestones and success metrics, and hand over at the end. Many clients start with a project and keep one or two of the engineers on afterwards; either way, the code and models are yours.
They are our own team, not a marketplace. Each has shipped AI systems on client work under our process: evaluation sets, model cards, documentation as part of done. For each placement you see the person's profile and relevant work, interview them yourself, and can run a paid trial sprint before committing to a longer engagement.
Our engineers are based in Pakistan (UTC+5). That gives full working-day overlap with Europe and the Middle East, morning overlap with the US East Coast and afternoon overlap with Australia. We agree core overlap hours up front and work asynchronously the rest of the day: written updates, recorded demos and clear hand-offs.
A monthly rate for full-time engineers, or an hourly rate for part-time and advisory arrangements, agreed per role and seniority before the trial. The rate includes sourcing, vetting, payroll and replacement, and there is no separate recruitment fee. We give ranges early in the conversation so nobody wastes time.
You do. Work happens in your repositories and cloud accounts, IP is assigned to you in the agreement, and any data you share is covered by an NDA and a data-processing agreement. Access is least-privilege and removed cleanly at the end of the engagement, with documentation left behind for your team.
Tell us. We start with a paid trial precisely so you can judge fit on real work. After that, if a placement is not delivering, or the engineer becomes unavailable, we replace them at no extra recruitment cost and manage a handover with documentation and pairing so your project does not stall.
Tell us the gap in your AI team
A short brief is enough. We will come back with a realistic shortlist, timing and rate range, or an honest note that a project or advisory arrangement would serve you better.