Motion Applied
AI Platform Engineer
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About us
The world is undergoing significant change, the greatest in generations.
At Motion Applied, our people and technologies are helping to create a more sustainable, intelligent and connected future. With more than 35 years of Formula One heritage and pedigree, we combine world-class engineering expertise with a relentless focus on innovation to solve complex challenges and deliver breakthrough performance for our customers.
Today, Motion Applied operates across multiple industries, applying advanced technology, software and engineering expertise to help organisations push the boundaries of performance, efficiency and reliability. From developing next-generation products to supporting critical customer programmes, our teams play an important role in enabling the future of mobility and intelligent systems.
Driven by legacy and powered by innovation, we are committed to creating an environment where talented people can grow, collaborate and make a meaningful impact while helping our customers achieve success.
Role Purpose
ATLAS is evolving from a telemetry and engineering data platform into an intelligent engineering decision platform.
The AI Platform Engineer will lead the development of the foundational AI capabilities required to enable AI-powered workflows, engineering copilots, knowledge discovery, and autonomous agent capabilities across the ATLAS ecosystem.
The role is responsible for designing and implementing the AI platform, integrating Large Language Models (LLMs), Retrieval Augmented Generation (RAG), knowledge graph technologies, and machine learning inference capabilities into ATLAS products and services.
This role will work closely with Product Management, Architecture, and Engineering teams to transform engineering knowledge into actionable intelligence.
Key Responsibilities
AI Platform Development
- Design and develop ATLAS AI platform capabilities.
- Build reusable AI services and frameworks.
- Develop AI orchestration services for ATLAS applications.
- Create scalable AI infrastructure and deployment patterns.
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AI Copilot & Agent Development
- Develop AI Copilot capabilities for engineering workflows.
- Build multi-agent workflows and orchestration frameworks.
- Implement tool-use capabilities and workflow automation.
- Develop mechanisms for AI reasoning, planning, and execution.
Retrieval Augmented Generation (RAG)
- Design and implement enterprise RAG solutions.
- Develop document indexing and semantic search capabilities.
- Implement contextual knowledge retrieval.
- Optimise prompt engineering and grounding techniques.
Knowledge Graph Integration
- Integrate knowledge graph technologies including Craxel.
- Model engineering entities, relationships, and context.
- Enable engineering knowledge discovery and navigation.
- Support digital thread and traceability initiatives.
LLM & AI Integration
- Integrate Azure OpenAI and other foundation models.
- Evaluate emerging LLM technologies.
- Manage model selection and optimisation.
- Implement AI governance and evaluation frameworks.
Machine Learning & Inference
- Integrate ONNX Runtime into ATLAS workflows.
- Support deployment of machine learning models.
- Enable edge and cloud inference scenarios.
- Develop model evaluation and monitoring processes.
Platform & API Development
- Build AI-focused APIs and microservices.
- Integrate AI capabilities with existing ATLAS services.
- Ensure scalability, observability and operational supportability.
- Collaborate with DevOps on deployment and infrastructure automation.
Key Deliverables
The successful candidate will contribute to:
- ATLAS AI Copilot
- Engineering Knowledge Assistant
- RAG Platform
- Craxel Knowledge Graph Integration
- Agent-Orchestration Framework
- AI Workflow Automation
- ONNX Model Runner Integration
- Future Intelligence Layer capabilities
Desired Experience
Essential
- Python development experience
- API and microservice development
- Experience working with LLMs
- Prompt engineering experience
- Cloud-native application development
- Distributed systems and service architectures
- Git-based development workflows
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Preferred
- Azure OpenAI
- Anthropic Claude
- LangChain
- LlamaIndex
- Semantic Kernel
- Vector databases
- Knowledge graph technologies
- ONNX Runtime
- Kubernetes
- Kafka
- Event-driven architectures
Technical Skills
Programming
- Python
- REST APIs
- SQL
- JavaScript/TypeScript (desirable)
AI & Data
- LLM integration
- RAG architectures
- Embeddings
- Vector databases
- Agent frameworks
- Prompt engineering
- Evaluation frameworks
Platform Technologies
- Azure
- Docker
- Kubernetes
- CI/CD
- Kafka
- Cloud monitoring
What we can offer you
In return for everything you bring to the table, we can ensure an exciting, challenging role in a dynamic business surrounded by some of the best people in their respective fields.
At Motion Applied we firmly believe it’s the relationships and friendships we create while working that make us special. We’re also aware that the world is changing and we are part of that change. We all want and need different things from our work and home lives, so, if you have commitments outside of work, we’re open to talking through flexible working options that work for you and us.
- Annual leave (25 days + bank holidays, pro-rated for part time colleagues).
- Enhanced Company Maternity, Paternity and Adoption leave and pay.
- Life assurance to the value of 4 times base salary
- Opportunity to join the Motion Applied Pension Plan
- Company funded individual private healthcare with the opportunity to extend to partner or spouse and/or dependents at a discounted rate.
- Electric car scheme – opportunity to drive a brand-new car in a more affordable way through this salary sacrifice scheme. Employees are eligible to join the scheme after successful completion of their probationary period.
Our values
- OWNERSHIP
- OPENNESS
- BRAVERY
- INGENUITY
- HUMILITY
Motion Applied are committed to Diversity, Equality and Inclusion (DEI) and promote DEI in all we do. Motion Applied are also members of the UK Government Disability Confident Scheme.
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