AI ENGINEERING LEAD – design & build end-to-end AI SOLUTIONS for leading global Wellness & Heal[...]

Acuity Consultants; City Of London; On-site; GBP 120,000 - 130,000; Full time

Job summary

A global HealthTech brand is seeking an AI Engineering Lead to design, build, and run end-to-end AI solutions. This role requires extensive experience in applied machine learning, data science, and quantitative analytics. You will lead AI initiatives across multiple countries, build production-grade systems, and mentor a team of engineers. The ideal candidate has a strong background in customer analytics and expertise in Python and SQL. The position is based in London with a competitive salary range of £120K – £130K.

Qualifications

• 8+ years in applied ML, data science, quantitative analytics, software engineering.
• Track record of shipping production AI solutions end‑to‑end.
• Experience leading complex AI initiatives.

Responsibilities

• Lead AI initiatives and shape AI delivery across the organisation.
• Design and ship production-grade AI systems.
• Engineer high-performance AI infrastructure.

Skills

Applied ML

Data science

Quantitative analytics

Software engineering

Customer analytics

Expert Python

Expert SQL

ML frameworks (sklearn, XGBoost)

ML frameworks (PyTorch, TensorFlow, LightGBM)

Cloud fluency

Tools

ML frameworks (sklearn, XGBoost, PyTorch, TensorFlow, LightGBM)

APIs

Job description

This is an exceptional opportunity for an ARTIFICIAL INTELLIGENCE ENGINEERING LEAD (AI Engineering lead) - with advanced Quantitative Analytical skills - to design, build, and run end-to-end AI Solutions for the world’s leading Wellness / HealthTech Business.

Based in LONDON, this AI ENGINEERING LEAD role offers a salary of £120K – £130K

THE COMPANY

This globally recognised brand is a HealthTech & Wellness company that blends smart technology and world-class facilities into transformative wellbeing experiences. They are a next‑generation HealthTech brand using intelligent tools, digital platforms, and science‑backed training environments to optimise how people move, recover, and live.

Their digital innovation journey is in full‑swing, and now at the point where AI INNOVATION will result in an even better experience for their 1 million consumers.

THE ROLE

The company is scaling AI safely, responsibly, and at speed across South Africa, the UK, Italy, Australia, Singapore, Thailand, and Qatar. They are building a world‑class team of AI Engineers who can design, build, and run AI solutions that millions of consumers use every day. This role is your chance to lead that journey.

As Lead AI Engineer, you’ll own entire problem spaces, lead mission‑critical AI initiatives, and shape how AI is delivered across the organisation. You’ll sit at the intersection of software engineering, data engineering, machine learning, and quantitative analytics, working end‑to‑end from problem framing through to deployment, monitoring, and scaling across multiple countries.

This is a hands‑on technical leadership role: you’ll build, mentor, architect, influence, and set the bar for how AI should be done in this global consumer business.

WHAT YOU’LL DO

Own and lead AI across an entire domain

• You’ll be the technical lead for areas like CRM & personalisation, operations optimisation, or digital coaching - defining roadmaps, making architectural decisions, and guiding teams through discovery, experimentation, and delivery.

Build production‑grade AI systems

• From prototypes to scaled deployments, you’ll design and ship systems that power real‑world experiences: personalised journeys, churn prediction, lead scoring, RAG‑based knowledge assistants, energy optimisation, operational AI tools, and more.

Data, modelling & experimentation

• Analyse and prepare data for AI use‑cases: consumer behaviour, CRM interactions, digital journeys, operations, marketing and sales, energy consumption, etc.; Build and evaluate models using appropriate techniques, including classical machine learning, LLM and generative AI; design and implement prediction models for use cases like churn, propensity, next best action, lead scoring, lifetime value. Set technical direction for modelling approaches. Support the creation of synthetic personas and segmentation using a mix of data‑driven and generative techniques. Develop and test marketing mix models and other measurement frameworks to understand and optimise marketing spend. Define evaluation strategy and acceptance criteria for your use‑cases, guide others in doing the same, and ensure that evaluation practices are consistent and repeatable across the team.

Partner across the business

• You’ll work with Marketing, Digital, Operations, Finance, and Legal to turn ambiguous problems into structured AI opportunities; define hypotheses and success metrics; run rapid A/B tests and pilots; align multiple teams around a single plan; communicate model limits, trade‑offs, risks, and value in plain language.

Engineer high‑performing AI infrastructure

• You’ll architect and build APIs, services, pipelines, integrations, guardrails, and monitoring frameworks - ensuring that AI is safe, reliable, scalable, cost‑efficient, and compliant.

Lead technical delivery across markets

• You’ll mentor engineers and data scientists, review critical designs, coordinate external partners, and ensure solutions are production‑ready before scaling globally.

Shape the AI engineering culture

• You’ll contribute patterns, templates, starter kits, documentation, playbooks, internal demos, training sessions, and hiring - raising the bar for the entire group.

REQUIRED SKILLS

• 8+ years in applied ML, data science, quantitative analytics, software engineering with meaningful AI / ML responsibility.
• A track record of shipping production AI solutions end‑to‑end.
• Experience leading complex AI initiatives and other engineers.
• Strong background in customer analytics, personalisation, ops optimisation, marketing optimisation, conversational AI, or related domains.
• Expert Python and SQL.
• Strong system design and engineering fundamentals.
• Hands‑on with ML frameworks sklearn, XGBoost, PyTorch, TensorFlow, LightGBM.
• Experience with LLMs, prompt design, RAG, evaluation frameworks.
• Familiarity with data engineering and modern MLOps (registries, pipelines, CI / CD, monitoring).
• Cloud fluency.
• Comfortable switching between discovery and delivery.
• Strong appreciation for privacy, governance, and responsible AI.