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Job Description Data & AI Portfolio Lead Job Location: Birmingham, Bracknell, Derry, Edinburgh, London, Manchester, Wakefield, Warrington Location Flexibility: Multiple Locations in Country Req Id: 12735 Posting Start Date: 9/25/26 At Fujitsu, our purpose is to make the world more sustainable by building trust in society through innovation. Founded in Japan in 1935, Fujitsu has been a pioneer in technology and innovation for decades. Today, as a world-leading digital transformation partner, we are committed to transforming business and society in the digital age.
With approximately 130,000 employees across over 50 countries, Fujitsu offers a broad range of products, services, and solutions. We collaborate with our customers to co-create solutions that drive enterprise-wide digitalization while actively working to address social issues and contribute to the United Nations Sustainable Development Goals (SDGs).
Reporting to: Head of UK Portfolio, Integration & Enablement
Location: UK based with travel as reasonably required including international travel.
Overview
The Data & AI Portfolio Lead is a critical leadership role within the UK Technology Group. It is responsible for driving the successful development, integration, and enablement of Fujitsu’s global & local portfolio within the UK market for Data & AI.
This position ensures alignment with strategic objectives and maximizes the impact of Fujitsu’s wider portfolio (Uvance / Europe / Other Countries / Partners) while leading local go to market, offering creation, and delivery of key performance indicators for Data & AI.
As a Portfolio Lead, you will play a pivotal role in shaping the future of our UK organization and ensuring our growth in the short, medium and long term to deliver our vision of becoming the UK leader in Sovereign, Data & AI solutions.
Scope of Role:
The Portfolio Lead for Data & AI will accountable for the definition and execution of the UK strategy and vision for data & AI. The scope of the role covers:
- Industry AI Solution
- AI Business Transformation
- Data Transformation
- Industry Aligned Applications
You will work closely with the Uvance, European andother local portfolio’s to ensure that Fujitsu UK has a coherent market leading portfolio for your area that supports the UK business in realising its vision to become the UK leader in Data & AI.
As Portfolio Lead you will provide leadership and managerment to product managers in your area to deliver on your vison and strategy ensuring our Data & AI offerings meet evolving customer needs and deliver business value.
You will work cross-functionally to innovate, optimise, and grow our portfolio, supporting Fujitsu’s purpose of building trust in society through technology and engage with Market Analysts Partners and Vendors as Fujitsu’s recognised expert.
Key Responsibilities:
- Data & AI Portfolio Strategy: Own the vision and strategy for the Data & AI portfolio to realise the UK Portfolio vision of being a leader in Data & AI (Roadmap / Go to Market / Story) aligned to the overall UK Data & AI strategy
- Data & AI Portfolio Development: Lead the development of a coherent market leading portfolio combining wider portfolio (Uvance / Europe / Partners) and local offerings
- Local Offering Management: Manage the end-to-end lifecycle and roadmap for UK products and services, from concept to retirement, focusing on desirability, business value, and feasibility.
- Stakeholder Management: Management of the stakeholder landscape for portfolio including global, Europe, Sales, Delivery, Presales and Partners for Data & AI
- Market Readiness & Operationalization: Ensure the UK sales and delivery teams are enabled to sell and deliver relevant Portfolio offerings.
- Marketing & Promotion: Lead internal and external promotion of offerings, collaborating with marketing, sales and analysts.
- People Management: Leading and managing a team of offering managers
Key Skills & Competencies:
- Strong leadership, influencing and strategic thinking skills to develop offerings that support long-term business growth.
- Effective people management skills to build and motivate virtual and line-managed teams.
- Expertise in portfolio management, product lifecycle management, go to market development and market enablement.
- Deep understanding of Data & AI and the future for this domain
- Excellent stakeholder management and communication abilities and an ability to navigate complex, matrixed environments and drive cross-functional collaboration.
- Strong analytical and problem-solving skills with a results-driven approach.
Qualifications & Experience:
- Extensive experience in portfolio management, product strategy, or technology enablement for a service/system integrator.
- Proven track record in leading initiatives within a large organization.
- Background in digital transformation, data platforms, AI, or enterprise technology solutions is highly desirable.
- Strong business acumen with the ability to translate strategy into execution.
- Experience working in a multinational or global enterprise environment.
Please note: It’s still worth applying even if you do not meet all the requirements above. We are passionate about investing in you and your career and if you have the transferable skills/ background Portfolio Management, Data & AI Strategy or Go To Market this could be the next opportunity for you.
Your benefits:
- 26 Days annual leave plus public holidays (3 flexible)
- Pension – Double matching contributions of up to 10%
- Life assurance
- Companywide incentive plans
- Your choices (Flexible benefits such as increased holidays/ travel/ dental critical illness and more)
- Perks at work – employee discounts
- Employee assistance programme/ virtual GP
- Role dependent benefits: Private medical / company car or car allowance
Recruitment process
The recruitment process consists of two stages of interview.
We are a Disability Confident Employer and will offer an interview to disabled applicants who meet the minimum/essential criteria for the role. Email [email protected] if you would like to apply through the Disability Confident Interview Scheme.
#Europe_Priority
Relocation Supported: No Visa Sponsorship Approved: No At Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. We believe that hiring people from a wide variety of backgrounds makes us stronger, not because it's the right thing to do, but because it allows us to draw on a wider range of perspectives and life experiences.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fujitsu, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Fujitsu AI Hiring
Fujitsu has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Birmingham, AL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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