Key Responsibilities:

• Delivers machine learning ops engineering tasks such as deployment, implementation, optimization, and maintenance of machine learning pipelines and models.
• Ensures pipelines support efficient data ingestion, preprocessing, model training, validation, deployment and monitoring.
• Implements scalable and robust machine learning solutions that can handle large volumes of data and complex models.
• Implements real-time inference with high availability and low latency.
• Creates strategic plans within span of control and implements them across one to two business domains.
• Ensures seamless integration of pipelines with continuous integration and continuous deployment (CI/CD) tools and workflows.
• Supporting and maintaining solutions in production (fixing bugs, make changes as required, maintaining models)
• Collaborates with cross-functional teams to integrate machine learning and business logic-based solutions into production systems
• Effectively communicates and applies machine learning engineering value, concepts, and strategies in various scenarios with stakeholders
• Recruits, hires, and mentors' top talent to build a high-performing MLOps team. Supervises, coaches, and guides direct reports
• Uses advanced knowledge of code management principles to follow architectural and governance guidelines

Requirements:

• 5 years of experience required in deploying and managing machine learning pipelines, or related work.
• Full English Fluency
• Experience in a leadership role within a fast-paced, technology driven environment
• Team: Data Scientist, Python Developers, Cross disciplinary (Underwriting, Actuary) 2 Direct Reports
• Insurance (PLUS), Healthcare, heavily regulated, audit

Technical & Business Skills:

• Possesses strong technical aptitude. In-depth knowledge of machine learning frameworks and libraries.
• Modern Oriented Language: Python (PLUS), Java, Typescripts, Ruby, Rust
• Familiar with DevOps practices and tools for continuous integration and deployment. (MUST)
• Experience in Production Support (Maintaining Models, Bug Fixes) MUST
• Collaborative with other areas, Translate, strong communication
• Business Logic Model, Real Time execution: Business Logic Based Solutions, writing code to determine system behavior. (MUST)
• Small volumes of data but fast execution
• Automated software and Model Testing (how to know model is behaving properly)

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If you're looking for the chance to leverage advanced technology to redefine the transportation landscape, enhance the customer experience, and improve people's lives: this is the opportunity for you. Join us and challenge your IT expertise and analytical skills to help create vehicles that are as smart as you are.

To meet the growing needs of the Customer analytics business, the team is looking for a self-motivated, technically proficient individual to craft and shepherd coherent solutions. This will require collaboration with a range of stakeholders to clarify requirements, establish pragmatic approaches, and support and articulate decisions over time. You will join a cloud architecture team that works closely with engineering teams and other architects across the organisation.

Responsibilities

Technical Requirements

• Extensive experience with Google Cloud Platform (GCP), specifically BigQuery, Vertex AI, Dataflow, Dataproc, Cloud Run, CloudSQL, Spanner and Apigee.

• Security & Networking: Strong understanding of cloud security protocols, IAM, encryption, and complex network topologies.

• Data Management: Proficiency in Enterprise Data Platforms, Data mesh architecture and data-driven architectural patterns.

• DevOps Tooling: Hands-on experience with GitHub, SonarQube, Checkmarx, and FOSSA.

• Software Engineering: Strong background in building Web Services and maintaining Clean Code standards.

Technical Leadership & Strategy

• System Design: Work with engineering teams to refine system designs, evangelising for horizontal scalability, resilience, and Clean Code compliance.

• Product Collaboration: Partner with Product Managers to decompose complex business needs into incremental, production-ready user stories within an Agile/Sprint methodology.

• Architectural Governance: Assess and document the rationale and tradeoffs for technical decisions; contribute to the broader Cloud Architecture team to improve global practices.

• DevOps Excellence: Utilise and improve CI/CD pipelines using GitHub and automated testing/security tools to maximise deployment efficiency and minimise risk.

Cloud, Networking & Security

• Secure Infrastructure: Serve as the primary architect for cloud solutions, ensuring 'Secure-by-Design' principles are applied across Google Cloud services (Dataflow, Dataproc, CloudRun, CloudSQL, Spanner).

• Advanced Networking: Design and optimise cloud networking configurations, including VPCs, Service Controls, Load Balancing, and Private Service Connect to ensure high availability and low latency.

• Cyber Security Oversight: Integrate security scanning and compliance into the architecture (utilising Checkmarx, SonarQube, and FOSSA). Proactively address vulnerabilities in distributed systems and AI models (e.g., OWASP Top 10 for LLMs).

• API & Data Contracts: Bolster 'Data as a Product' practices by enforcing strict API standards and data contracts to ensure seamless, secure interoperability between services.

• FinOps & Cost Optimisation: Drive fiscal responsibility by right-sizing GCP resources and optimising Generative AI architectures (token management/model selection) to maximise ROI.

• SRE & Performance Tuning: Apply Site Reliability Engineering principles to ensure high availability, minimise system latency, and lead root-cause analysis for complex, distributed system failures.

• DevSecOps & Problem Solving: Integrate security automation into CI/CD pipelines to ensure 'Secure-by-Design' deployments while solving complex architectural trade-offs between speed, scale, and risk.

• Continuous Learning: Stay at the forefront of AI research, specifically regarding autonomous agents, prompt engineering etc

Nice to Have

• AI development tools and frameworks (e.g., LangChain, LangGraph, or Agent Dev Kit) to accelerate the delivery of intelligent applications.

• Agentic & GenAI Design: Lead the architectural design of Agentic AI systems (multi-agent orchestration) and Generative AI solutions, including Retrieval-Augmented Generation (RAG) patterns and LLM integration.

• Kubernetes (GKE): Experience managing containerised workloads at scale.

• Kafka/Event-Driven Design: Experience with high-throughput messaging and event-driven architectures.

• MLOps: Familiarity with the end-to-end lifecycle of machine learning models in production.

Qualifications

You'll have...

• Requires a bachelor's or foreign equivalent degree in computer science, information technology or a technology related field

• 5+ years of Software engineering experience using Java or Python developing services (APIs, REST, etc.)

• 2+ years of experience with Google Cloud Platform or other cloud service provider (AWS, Azure, etc.) and associated cloud components.

• Experience designing/architecting and running distributed systems in a production environment

• STRONG communications skills and cognitive agility - ability to engage in deep technical discussions with customers and peers, become a trusted technical advisor, and maintain good documentation

Even better, you may have...

• Master's degree in computer science, electrical engineering or a closely related field of study

• Familiarity with a breadth of programming languages, platforms, and systems

• Experience with asynchronous messaging and eventually consistent system design

• An agile, pragmatic, and empirical mindset

• Critical thinking, decision-making and leadership aptitudes

• Good organisational and problem-solving abilities

• MDM, Entity Resolution, Customer Analytics and Marketing Analytics experience is a huge plus.

You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!

As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder…or all of the above? No matter what you choose, we offer a work life that works for you, including:

• Immediate medical, dental, and prescription drug coverage

• Flexible family care, parental leave, new parent ramp-up programs, subsidised back-up child care and more

• Vehicle discount programme for employees and family members, and management leases

• Tuition assistance

• Established and active employee resource groups

• Paid time off for individual and team community service

• A generous schedule of paid holidays, including the week between Christmas and New Year's Day

• Paid time off and the option to purchase additional vacation time.

For a detailed look at our benefits, click here: Benefit Summary

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