AI Teams and Outsourcing
Hire AI Developers Who Ship Production Outcomes
Your roadmap needs senior ML engineers, LLM architects, and AI Ops specialists, not a bench of generalists. Redefine matches your team with production-grade AI developers in 48 hours, governance-ready from day one. Explore the AI Teams and Outsourcing Hub or AI consulting pricing before you brief us.
48h
to matched engineers
14
days to first sprint, average
100%
IP stays with you
Submit briefCall within 48 hoursTeam matched in 5 daysSprint 1 starts within 2 weeks
Your team's time commitment across an AI development engagement is typically 2 to 3 hours per week: one sprint review, async feedback on model outputs, and a fortnightly architecture sync. We handle standups, code reviews, and deployment operations.
Average time to first sprint
14 days
Brief submittedSprint 1 live
The challenge
What slows AI teams down
Most engineering organisations spend six months hiring one senior ML engineer. During that window, your competitors are shipping. The cost is not the salary. It is the quarters you lose to inaction.
The Redefine way
- Team matched and briefed in 48 hoursPre-vetted senior engineers with production AI credentials, available to start immediately.
- ML, LLM, and AI Ops specialists onlyEvery developer on the bench has shipped production AI: model training, retrieval-augmented generation pipelines, or inference infrastructure.
- Full IP transfer and documented handover on closeArchitecture diagrams, runbooks, and all code transferred to your repositories on engagement close.
- Milestone-gated delivery with outcome service level agreementsSprint reviews every two weeks. No milestone hit, no next invoice raised.
The old way
- 4 to 6 months to hire one senior ML engineerSourcing, screening, and notice periods stack before any code is written.
- Generic development shops with no AI specialisationTeams that call themselves AI-ready but have never shipped an LLM pipeline in production.
- No governance or handover structureContractors who leave with the code, context, and architecture in their heads.
- Billing by the hour with no outcome accountabilityScope creep with no milestone checkpoints or delivery service level agreements.
Who this is built for
Your role shapes the team we build
Select your context below. The team composition, engagement model, and delivery structure all adapt to how you work.
What you need
- Governance-ready team from day one, no ramp risk
- Service level agreement-backed delivery with milestone-gated billing
- Dedicated team extension that plugs into existing infrastructure
Typical team composition
Lead ML EngineerSenior
LLM ArchitectSenior
AI Ops EngineerMid
Data Pipeline EngineerMid
Active Engagement
Sprint 3 of 6Retrieval-Augmented Generation Pipeline v2Shipped
Inference Latency OptimisationIn Review
Fine-tuning Data PipelineQueued
48h
Average response
3 weeks
Average ramp
100%
Service level agreement met
What you need
- LLM specialists with shipped product experience
- Fast iteration cycles: ship evaluations, not just features
- Prompt engineering and evaluation framework ownership
Typical team composition
LLM EngineerSenior
Prompt EngineerMid
ML Evaluation SpecialistSenior
LLM Evaluation Runs
GPT-4o baseline
87.2% accuracyProd
Fine-tuned v2.1
91.4% accuracyTesting
Retrieval-Augmented Generation with reranker
89.8% accuracyPending
Fine-tuned v2.1 ready for staging deployment
What you need
- First AI sprint live in 14 days without a full-time hire
- Flexible scope: scale up or down by sprint
- Architecture advisory included in the engagement
Typical team composition
Full-Stack AI EngineerSenior
AI Architect (advisory)Principal
Scales with roadmapFlexible
14-Day Onboarding Track
1
Day 1 to 2: Brief and match
Team selected and introduced
2
Day 3 to 5: Architecture scoped
Stack decisions locked, repository access granted
3
Day 6 to 14: First sprint ships
Working prototype or pipeline in production
Sprint 1 delivered · 14 days from signed brief
What you get
Full-spectrum AI development capability
Every discipline your AI roadmap requires, available as a unified team or individual specialists, depending on where you are in your build.
ML Engineering
Model training, fine-tuning, and evaluation pipelines. PyTorch, JAX, and Hugging Face ecosystems. Production serving with latency budgets your product requires.
LLM Architecture
Retrieval-augmented generation system design, prompt engineering, context management, and model routing. Covers OpenAI, Anthropic, Mistral, and open-weight models in production.
LangChainLlamaIndexPineconeWeaviate
AI Ops
Model deployment, monitoring, and retraining pipelines. MLflow, Weights and Biases, and cloud-native infrastructure that keeps your models healthy in production.
Model serving uptime99.7%
Average inference P99Under 240ms
Drift detectionAutomated
Cost per 1M tokensOptimised per run
Data Engineering
Feature stores, vector indexing, training data curation, and batch pipeline orchestration. Airflow, Prefect, and Spark at scale.
Three engagement models
Dedicated Team
Full sprint team embedded in your workflow. Fixed monthly scope with bi-weekly delivery checkpoints.
Best for: ongoing AI product development
Project-Based Sprint
Fixed scope, fixed price, fixed timeline. Defined deliverable from scoping call to shipped output.
Best for: minimum viable product and proof-of-concept builds
Staff Augmentation
One or two senior specialists integrated into your existing team. Time and materials, month-to-month.
Best for: specialist skill gaps
Proof of delivery
Results a technical team delivered
From the AI case studies archive and the AI services frequently asked questions.
Corporate Gear
Ecommerce and Business-to-Business
Global corporate branding and apparel ecommerce platform serving enterprise clients at scale.
The problem
The platform required a comprehensive technical overhaul: usability barriers were limiting lead generation, conversion testing infrastructure did not exist, and the underlying architecture could not support enterprise-grade personalisation or accessibility requirements. The engineering scope covered headless commerce, ERP integration, ADA compliance, and A/B testing frameworks across a high-traffic storefront.
The result
$120M
annual revenue reached
A/B and multivariate testing identified high-performing layouts and calls to action. Navigation, accessibility, and user experience were rebuilt to reduce friction across every conversion path. Search engine optimisation improvements, personalised landing pages, and security hardening supported enterprise-scale transactions. The result: annual revenue scaled to over $120 million and the platform secured a leading competitive position in its category.
Significant
increase in lead generation and conversion rates
Full IP transfer
all code, architecture, and documentation retained by client
Integration architecture
How your hired AI team plugs in
Your infrastructure stays yours. The team integrates via standard engineering protocols: your repositories, your continuous integration and continuous delivery pipeline, your cloud accounts, with governance and IP ownership maintained by you throughout.
Your repositories, your rules
Engineers are added as collaborators to your existing GitHub or GitLab workspace. Branch policies and code review standards are yours.
IP stays with you from line one
Full IP assignment in the engagement contract. No licensing complications on close.
Cloud accounts remain in your control
Engineers operate within your AWS, GCP, or Azure environment. Temporary access provisioned and revoked per project phase.
Observability and model monitoring included
MLflow, Weights and Biases, or your preferred tooling. Drift detection and automated alerting configured during the first sprint.
Why Redefine
What sets senior AI teams apart
Other implementation partners optimise for headcount and hourly billing. Redefine optimises for shipped outcomes and knowledge transfer.
Redefine AI Developers
- Team matched and briefed in 48 hoursSprint 1 live within 14 days of signed brief
- ML, LLM, and AI Ops specialists with production proofEvery engineer on the bench has shipped AI in production
- Milestone-gated billing aligned to delivered outputsNo milestone shipped, no invoice raised
- Full architecture documentation and runbooks on closeYour team can operate everything independently from day one after handover
- Explicit IP assignment in contract, effective from day oneNo ambiguity. Everything built belongs to you.
Typical partner
- 4 to 6 week onboarding with no sprint accountability
- Generic development teams repositioned as AI-ready
- Billing driven by hours logged, not milestones hit
- No documented handover. Context leaves with the team.
- IP ownership ambiguous without explicit contract terms
Common questions
Before you brief us
Straight answers on onboarding speed, seniority, IP, scaling, stack coverage, and pricing.
More questions? See the full AI services frequently asked questions.
After your brief is submitted, the matched engineer or team is introduced within 48 hours. Repository access, sprint tooling, and communication channels are set up within the first 2 days. The first working sprint begins within 5 to 7 business days of sign-off, with Sprint 1 delivered within 14 days of the engagement start date.
The bench is weighted toward senior and principal engineers (5 or more years with production AI experience). Mid-level specialists are available for specific pipeline and data engineering work. There are no junior developers in client-facing roles. Every engineer has at least one shipped AI system in production before joining the bench.
You do. IP assignment is explicit in the engagement contract and takes effect from day one of the engagement. On close, you receive a full handover package: all code in your repositories, architecture diagrams, runbooks, environment configuration, and access credentials. Nothing leaves with the team.
Yes. Team composition is reviewed at each sprint boundary. If a sprint requires an additional specialist (a data engineer for a new pipeline, for example), they are added to the roster for that sprint. Scale-down works the same way: if a role is complete, the billing stops at the next sprint boundary, not at contract end.
Python is the primary language across ML, LLM, and data engineering work. Framework coverage includes PyTorch, JAX, Hugging Face Transformers, LangChain, LlamaIndex, FastAPI, and Ray. Orchestration covers Airflow, Prefect, and Dagster. Vector stores include Pinecone, Weaviate, Qdrant, and pgvector. Cloud deployment on AWS SageMaker, Google Vertex, and Azure ML. Model monitoring via MLflow and Weights and Biases.
Pricing is scoped before any work starts, with line-by-line sprint costing. Dedicated team engagements use a fixed monthly retainer per sprint cycle. Project-based engagements use a fixed-price scope with milestone billing. Staff augmentation uses a daily or monthly rate per specialist. See AI consulting pricing for reference ranges, or submit a brief for a scoped proposal within 3 days.
Fit check
Is this the right fit for you?
This is a fit if you are
- A Chief Technology Officer or VP Engineering with a defined AI roadmap and no available senior engineers to staff it
- A product team that needs LLM or ML capability without a full-time hire right now
- A founder who needs to ship an AI proof of concept in 14 days, not 14 months
- An engineering organisation that retains full IP and wants governance documentation on day one
This is not a fit if you need
- Junior or offshore development at lowest possible daily rates
- Pure data science research with no production delivery timeline
- A body-shopping arrangement with no outcome accountability
- An engagement where the partner, not you, retains the IP
Not sure where you land? Tell us your situation and we will be straight with you. Submit your brief below.
Get a scoped proposal
Talk To An AI Staffing Advisor
Submit your brief below and receive a scoped team proposal with line-by-line sprint costing in 3 business days. No commitment. No pitch.
- Matched engineer or team introduced within 48 hours
- Scoped proposal with sprint costing in 3 days
- Sprint 1 starts within 2 weeks of sign-off
- Full IP assignment from day one, documented in contract
Brief received
We will review your situation and send a scoped proposal with sprint costing within 3 business days. Expect a call within 48 hours.
48 hour response
Proposal in 3 days
47 projects done
IP: yours, always
Ready to start?
Your first AI sprint in 14 days
Describe your roadmap, get a scoped team, and start shipping. No commitment. No pitch.
Submit brief → call within 48 hours → team matched in 5 days → Sprint 1 starts within 2 weeks
48 hours
Team introduced after brief submitted
14 days
Average time to first sprint delivery
100%
IP retained by client from day one
47
AI projects completed and handed over