Hire AI Developers
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What an AI developer does?
An AI developer is someone who designs, builds, and deploys systems powered by foundation models, machine learning models, or custom AI models. The role covers the full lifecycle of AI systems, from data preparation, model selection and adaptation to evaluating output quality, integrating with existing systems, and monitoring performance once the system is live.
When you hire AI developers from 10Pearls in the UK, you gain specialists backed by enterprise architecture, data, cloud, security, and governance expertise. They work within your operational and compliance constraints to take AI from use case to production.
How much does it cost to hire AI developer?
- Seniority & skillset of AI developers and how scarce those skills are
- Engagement length
- Scope of responsibility and delivery ownership
- On-shore, nearshore, and offshore talent availability
- Regulated industry and security experience
- Compliance scope, data readiness, and integration complexity
Dedicated AI developer
£35 to £80 per hour
£5,500 to £13,000 per month
A pre-vetted resource embedded in your team, often with technical and delivery oversight provided by the supplier. It’s a fast way to add AI expertise to your team when you have clarity about what skills you need.
AI pod
£28,000 to £80,000 per month
A self-contained team of around four to eight members, including dedicated AI developers supported by a shared architect, QA, and delivery lead. These pods take ownership of a defined delivery segment.
Full-cycle AI delivery team
Starts from around £48,000 to £65,000 per month
A comprehensive team that takes end-to-end ownership of an AI programme, comes with dedicated delivery leads, architects, MLOps engineers, covering development, integration, governance, monitoring and scaling.
Disclaimer: The ranges are estimated based on the UK and European market rates. The actual costs may vary beyond these ranges based on the latest market trends and factors mentioned above. Infrastructure, compute, and licensing costs are not included.
In-house, marketplace, offshore shop, or enterprise partner?
| In-house hires | Freelance marketplace | Offshore dev shops/Generalist dev shops | 10Pearls – Enterprise partner | |
|---|---|---|---|---|
| Time to productive work | 2 to 6 months for sourcing and onboarding | Days, if you do the vetting yourself | Weeks, depending on talent availability | Days to weeks, depending on your vetting pace |
| Breadth of skills you reach | One skill set per hire | Whichever specialist you sourced | Limited to that shop's bench | Development, integration, MLOps, data and governance in one team |
| Regulated-industry readiness | Depends on individual and internal AI maturity | Rare, and hard to verify | Varies widely by vendor. Influenced by experience and governance approach | 20+ years in healthcare, finance and telecom |
| Compliance ownership | You build and own it | You carry all of it | Usually, yours to verify | ISO 27001 certified, with HIPAA, GDPR and NIST AI RMF controls built into the architecture |
| Architect and delivery lead | You provide both | Not included | Usually present, architects often billed separately | Included in every engagement |
| IP and confidentiality | Employment contract | Platform terms, variable by contract | Contractual, varies by vendor | NDA-backed, IP assigned to you |
| Continuity if someone leaves | Back to month one | Context leaves with them | Depends on bench depth and delivery practices | Backfilled with documented handover |
| If the fit is wrong | A months-long employment problem | Restart the search yourself | Renegotiate with the vendor | Replaced, with continuity preserved |
| Capability left with your team | Stays, as long as they do | None by design | Varies, often untracked | Structured knowledge transfer built in |
| Scaling up or down | Slow in both directions | Fast up, less reliable | Moderate in both directions | Rapid in both directions, no new procurement cycle |
| Accountability for the outcome | Yours | Yours. Freelancer only accountable for assigned tasks. | Shared, loosely defined | Ours under full-cycle delivery, shared under staff augmentation. Named lead either way. |
| Cost profile | Highest fixed cost | Lowest hourly, highest variance | Low hourly rate | Mid to premium hourly rate |
You are in great company
10Pearls developers have built solutions and platforms for a wide range of enterprises, from FTSE 100 and Fortune 500 companies to start-ups and scale-ups in the UK and Europe.
Why enterprises choose 10Pearls for AI talent
Enterprise accountability
We take accountability for the work of every AI developer you hire with us, with clearly defined goals and transparent oversight.
Built-in compliance
As an ISO 27001-certified company, our developers are used to building security, compliance, and other governance controls into architecture.
Platform agnostic
Our engineers work across GPT, Claude, Gemini, and the native AI tooling in AWS and Azure, so model choice follows your use cases and environment.
Production engineering
Our AI developers go beyond proofs of concept, covering the integration, testing, monitoring, and MLOps needed to run AI reliably in production.
Flexible scaling
From one AI developer to a full delivery team, we help you scale capacity up or down rapidly without disrupting project knowledge and continuity.
Global delivery, local alignment
You work with delivery leadership in your own time zone, who are supported by nearshore and offshore engineering teams for optimal delivery flexibility.
Specialized AI developers you can hire
Accelerate AI adoption by hiring specialised AI talent your organisation needs, from an enterprise partner that takes accountability for outcomes. Our AI developers for hire span across multiple disciplines and specialisms, covering the breadth and complexity that many enterprise AI projects need.
Forward-deployed AI engineers (FDEs)
FDEs work inside your operational context and constraints to build, integrate, and align AI solutions for use cases they help you select.
LLM developers
LLM developers have expertise across foundation models, integration, and data architecture, and build AI systems grounded in your enterprise knowledge.
Agentic AI engineers
Agentic AI engineers experienced in building, deploying, monitoring, and scaling everything from individual agent architectures to governed multi-agent systems.
Machine learning engineers
ML engineers develop, train, and run ML models, build data pipelines, monitor models in production, and modify and scale as ML systems as requirements evolve.
Computer vision (CV) engineers
CV engineers build vision systems and image and video pipelines for inspection, object tracking, and OCR at production throughput, on device or in the cloud.
NLP engineers
NLP engineers build language systems for extraction, classification, and semantic search capable of handling inconsistent formats and domain vocabulary.
Conversational AI developers
Conversational AI developers build assistants and voice agents, with expertise in intent recognition, conversation design, multilingual support, and human handover.
MLOps and AI platform engineers
These engineers take ownership of the platform AI runs on, from pipelines and model registries to inference infrastructure, monitoring and drift detection.
AI data engineers
AI data engineers make enterprise data usable for AI, with source integration, quality validation, PII handling, and retrieval-ready structures.
Data scientists
Data scientists help enterprises understand what drives outcomes from AI initiatives via causal analysis and experiment design to quantify the business case.
The skills our AI engineers bring
Foundation model adaptation
- Supervised fine-tuning on domain data
- Parameter-efficient methods including LoRA and QLoRA
- Preference tuning and alignment to your standards
- Curation of proprietary training datasets
- Supervised fine-tuning on domain data
Retrieval & grounding
- Citation traceability, so every answer's source is auditable
- Chunking and embedding strategy based on document types
- Vector store selection and semantic search (hybrid keywords)
- Reranking sources to align optimal context to the model
Agentic systems
- Human-in-the-loop checkpoints on high-stakes actions
- Tool use, function calling and permission scoping
- Single and multi-agent orchestration
- Model Context Protocol integration
Classical machine learning
Purpose-built machine learning remains effective for forecasting, scoring, classification, and other problems that do not require a foundation model.
- Model development, selection, and feature engineering
- Developing applications around ML models
- Ensemble methods and model optimisation
- Hyperparameter optimisation
Deep learning architectures
- Sequence models for time-series forecasting
- Convolutional networks for vision tasks
- Transformer architectures for language
- Autoencoders for anomaly detection
Evaluation & observability
The quality of AI systems is measured before release and monitored after deployment to ensure that regressions, drifts, and unexpected behaviour are timely identified.
- Designing both online and offline evaluations
- Concept and data drift detection
- A/B testing of model and prompt changes
- Model versioning and rollback when a release regresses
Data engineering for AI
- Pipeline design for training and inference
- PII handling, redaction, and access control in line with UK GDPR
- Data quality validation before it reaches a model
- Feature stores and synthetic data if real data is scarce
Deployment & scale
Production and scalable AI systems need infrastructure that can maintain performance, availability, and remain predictable as usage changes.
- Containerised inference and autoscaling
- GPU scheduling and capacity planning
- Continuously optimise cost-per-inference
- Edge and on-device deployment (for latency/privacy)
Prompt & context engineering
- System prompts and structured outputs
- Context, tool, and schema design
- Prompt versioning and regression testing
- Memory and state design for multi-turn and multi-step workflows
Intelligent automation
- Exception and approval handling
- Document processing and data extraction at volume
- Combining RPA orchestration with AI
- Workflows that require judgement, not rules
AI models & platforms we work with
GPT (OpenAI)
Best suited to: Complex reasoning, code generation, multi-step tasks How we use it
- Copilots and internal assistants
- Retrieval workflows over enterprise content
- Structured output for system-to-system use
- Code generation and review tooling
Claude (Anthropic)
Best suited to: Long documents, analysis, context-heavy work How we use it
- Contract and policy review at volume
- Compliance and audit-evidence workflows
- Summarisation across large document sets
- Technical review and documentation
Gemini (Google)
Best suited to: Multimodal reasoning, mixed text and image inputs How we use it
- Document and image understanding pipelines
- Claims, forms and scanned-record processing
- Orchestration across mixed content types
- Workloads already running in Google Cloud
Open-weight models
Best suited to: Private, on-premise and cost-sensitive deployment How we use it
- Deployment where data cannot leave your environment
- Fine-tuning on proprietary data
- Predictable inference cost at high volume
- Model selected per engagement, no vendor lock
Microsoft Copilot
Best suited to: Microsoft 365 and Azure environments How we use it
- Workflow automation across Microsoft 365
- Microsoft Graph integration
- Organisational knowledge retrieval
- Extending Copilot with custom capability
Perplexity
Best suited to: Research workflows that require citations How we use it
- Retrieval where answers must be traceable to a source
- Competitive and market monitoring
- Domain-aware research tooling
Grok (xAI)
Best suited to: Real-time signal handling, low-latency inference How we use it
- Operational and telemetry intelligence
- Live monitoring where latency matters
- High-throughput inference workloads
Tools & technologies
Model platforms
- Amazon Bedrock
- Amazon SageMaker AI
- Azure AI Foundry
- Google Vertex AI
- Oracle OCI Generative AI
- Hugging Face Inference
Orchestration & agents
- MCP servers and connectors
- LangChain
- LangGraph
- OpenAI Agents SDK
- Claude Agent SDK
- Microsoft Copilot Studio
- Semantic Kernel
Retrieval & vector stores
- Pgvector
- Pinecone
- Azure AI Search
- Amazon OpenSearch
- Databricks Vector Search
Data platforms
- Microsoft Fabric
- Google BigQuery
- Apache Spark
- Snowflake
- Databricks
- Airflow
- dbt
MLOps & observability
- Azure Machine Learning
- MLflow
- Open Telemetry
- Weights & Biases
- Lang Smith
- Grafana
Machine learning frameworks
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face Transformers
- spaCy
- OpenCV
Vision, speech & document AI
- Azure Document Intelligence
- Google Cloud Vision
- Amazon Rekognition
- Amazon Transcribe
- Whisper
- YOLO
Governance & security
- Amazon Bedrock Guardrails
- Azure AI Content Safety
- NVIDIA NeMo Guardrails
- Microsoft Presidio
- Microsoft Purview
- Open Policy Agent
- SHAP
How do you hire AI developers from 10Pearls?
Discovery & skills alignment
The discovery focuses on your AI goals, team dynamics, digital infrastructure, and talent gaps to identify the right fit for you, whether it’s an individual AI developer or a team of specialists.
Screening & onboarding
The optimal candidates are shortlisted for you to vet as per your technical and communication requirements. Selected ones are onboarded to your teams with a clearly defined scope and expectations, environment setup, and a structured handover into your tools and workflows.
Agile delivery & MLOps setup
Our AI developers adhere to the principles of agile development and support MLOps practices for better lifecycle management of AI deployments. This includes tooling like evaluation harnesses, observability, and prompt and model versioning.
Knowledge transfer & monitoring
Monitoring is built into most AI systems, so drifts and regressions are caught early. Structured knowledge transfer runs across the engagement, instead of specific checkpoints. This ensures developer or team configuration changes don’t undermine your AI adoption.
Scale up or optimise
Our developers help optimise, modify, and scale AI systems as businesses need to change or grow. The teams can be scaled up seamlessly as well to support your evolving AI needs.
Industries we staff AI teams for
AI engagement models
We offer three different engagement models to serve different talent gaps and development needs enterprises might have, along with the flexibility to switch between models when these needs shift.
Dedicated AI developers
Bridge talent gaps in your current teams or hire AI developers with specific expertise your AI initiative needs. This model enables quick onboarding, supports direct collaboration, and focuses on relatively narrow delivery goals.
Full-cycle AI delivery
We lead the full journey, from shaping requirements and architecture to AI development and MLOps. Ideal when your plans go beyond finding artificial intelligence developers and require a full execution partner.
Hybrid & distributed delivery
Blend onshore leadership with distributed teams to achieve speed, cost balance, and continuity. This approach works well when you expect evolving needs from a single hire ML developer to a fully distributed AI team.
Security, compliance, and IP protection
Certifications & recognitions
Frameworks we design and build to
Our AI developers build AI systems to compliance requirements and regulatory standards that apply to your operations:
Regulatory
- EU AI Act
- UK GDPR and EU GDPR
- NHS Data Security and Protection Toolkit (replaces HIPAA, which does not apply in the UK)
Standards & frameworks
- NIST AI Risk Management Framework (RMF)
- ISO/IEC 42001
- SOC 2
Contractual protections
NDA
Each engagement is bound by NDAs, whether you hire a single developer or a full team containing expertise across several disciplines. These agreements account for AI-specific clauses too, including permitted models and data sharing.
IP ownership
All work product is assigned to you: source code, prompts and system prompt architecture, evaluation datasets, retrieval configurations, and the documentation behind them.
Access control
Data handling
Outcomes you can expect
Fast onboarding
Our talent integrates thoughtfully and begins contributing early, helping you maintain momentum.
Platform flexibility
We guide you across GPT, Claude, Copilot, Grok and more ensuring you move forward without unnecessary lock-in.
Engineering maturity
Developers bring proven engineering discipline, building solutions that are stable, scalable and ready for real-world use.
Responsible + reliable AI
We help you build AI that's secure, well-governed, and aligned with your business and compliance needs.
Frictionless integration
Our people work naturally within your tools, culture, and workflows, operating as an extension of your team.
Smart decision support
You get clarity at every stage - from feasibility to sequencing - so decisions are grounded in context and value.
Scalable capacity
Easily expand from individual contributors to full squads as needs evolve, without disrupting delivery.
Cost-efficient growth
Access global talent and flexible models that help you scale responsibly and sustain value over time.
AI developer case studies
case study - artificial intelligence
Healthcare Support Platform with Agentic AI
10Pearls developed an agentic AI virtual assistant designed to enhance caregiver support through multilingual support, sentiment analysis, and a sophisticated knowledge management system.
case study - artificial intelligence
Agentic AI Financial Assistant
Optimizing financial operations with an AI-powered smart assistant designed to automate workflow and enhance decision-making.
case study - artificial intelligence
Automated Object Detection System
Streamlining property appraisals with an AI-driven platform that automates image analysis and object detection for greater speed and precision.
case study - artificial intelligence
AI-Powered Precision Agriculture
Driving innovation in agriculture technology by delivering precise microclimate insights from AI-powered prediction models.
What our clients say
Awards and recognitions
FAQs about hiring AI developers
How much does it cost to hire AI developers in the UK?
The cost of hiring AI developers may range from a few thousand pounds a month to six figures a month based on developer count, hourly rate based on region and specialisation, engagement length, developer experience, domain/industry-specific expertise, and complexity of the project.
How quickly can an AI developer start?
From a few days to a few weeks when you hire AI developers from an enterprise partner like 10Pearls that maintains a bench of pre-vetted, experienced AI developers. The timeline heavily depends upon your vetting process.
How do you vet AI developers?
10Pearls vets AI developers based on the breadth and depth of their AI capabilities, past project experience, industry-specific expertise, communication skills, cultural alignment potential, and their compliance and security habits, to ensure optimal alignment to clients’ needs.
What happens if the developer is not the right fit?
If a developer is not the right fit, 10Pearls aligns multiple, pre-vetted replacements that clients can vet and select from. We accelerate the onboarding of new developers through structured handover and contextual alignment to reduce disruption from the change.
Can I hire AI developers for a short-term project?
Yes. 10Pearls allows you to hire AI developers for projects of any length or complexity. We also have processes and guidelines in place if projects are closed prematurely or extended beyond the original timeline.
Can your AI developers work in our time zone?
Yes. We support full UK (GMT/BST) time zone alignment or overlap of sufficient hours through nearshoring in Europe or shift adjustment of offshore AI developers.
How do you protect our data and intellectual property (IP)?
Every engagement is NDA-backed before access is granted. Developers work inside your data handling constraints, under role-based access control adjusted to your requirements and in line with UK GDPR and the Data Protection Act 2018.
What is the difference between an AI developer and an ML engineer?
AI developers build applications, solutions, and automations on top of foundation models and integrate them into target environments, processes, and workflows. Machine Learning (ML) engineers build, design, and train ML models on context-specific data, often building data pipelines needed for training, maintenance, and optimisation of these models and applications based on them.
Can I hire AI developers if we have no in-house AI talent?
Yes. Our AI developers bring core capabilities into your environment, from data preparation and legacy integration to drift monitoring and AI solution evolution, across the full lifecycle of AI systems. However, based on your AI needs and the current state of your data and legacy systems, this may require hiring AI developers across multiple domains.
Can your developers integrate AI into our existing application?
Yes. Our developers bring a combination of legacy modernisation, custom middleware and API development, and integration capabilities, allowing them to integrate AI systems with a range of existing applications and legacy systems.
Which AI models and platforms do your developers work with?
Our AI developers work across a wide range of proprietary and open-source foundation models, AI platforms, native tooling of major cloud platforms, ML frameworks, MLOps tooling, data platforms, vector databases, and specialist models for vision and speech.
Do you provide documentation and knowledge transfer?
10Pearls has established practices for knowledge transfer and document handover across enterprise deliveries. Since each project is different and enterprises have varying in-house AI capabilities, the knowledge transfer is modified to meet their specific needs and may be supplemented with training and change management.