Senior, Forward Deployed Engineer (Digital Twin & Optimization)

Description

The company's Enterprise AI team builds AI solutions meant to prove their value quickly and then scale into production. The team pairs forward deployed engineers and full-stack engineers with scientific, clinical and business colleagues, and measures its work by outcomes for patients.

The company is a global biopharma business whose purpose is to unite science, technology and talent to get ahead of disease. It is committed to a diverse and inclusive team and welcomes candidates from all backgrounds, including groups under-represented in technology, science and pharma.

This role sits in Chemical Manufacturing and Control (CMC), the function that turns a validated molecule into a reliably made, quality-assured medicine. The function's current aim is to double New Product Introductions (NPIs).

You will be embedded in the team shaping how AI changes process development across the company's sites. The work centres on in-silico process models and digital twins for molecule process development, including foundation models, platform fitness models, tech transfer twins, scenario planning and scheduling optimization. You will build models yourself, aiming for platform models and end-to-end solutions rather than one-off point solutions. Process modelers stay the process experts and co-own the models. You will bring AI modelling and engineering that makes building, deploying and using models far more efficient, and you will mentor the engineers around you.

The role is a senior forward deployed engineer position. You work inside the business unit, prototype at a pace of days to weeks, and leave with working software and a defensible value case. Most prototyping is AI-augmented, so AI coding assistants and multi-agent workflows handle routine code while you focus on framing, judgement and validation. You hand proven solutions to the Senior Solutions Engineer for scale-up and do not carry use cases into long-term operations.

Responsibilities

• Own high-priority business unit use cases from problem framing to a working prototype, working in days-to-weeks cycles, and form a clear view on whether each should continue.
• Ship first production versions yourself where the path is clear, before the Senior Solutions Engineer takes the work to scale.
• Run AI agent workflows for planning, coding, testing, review and documentation, choosing the right level of automation for each engagement.
• Build with AI coding assistants by default, supplying judgement, course correction and validation.
• Work alongside business unit teams, including joining their standups, learning the domain and prototyping on real data and real workflows.
• Hand over proven use cases to the Senior Solutions Engineer with working code, architecture rationale, open risks and scaling concerns.
• Start governance conversations on Code Orange, Responsible AI and the Accountability Report during prototyping, so the productionisation team does not inherit governance debt.
• Add reusable agents, prompt templates and reference architectures to the organisation's pattern library after each engagement.
• At the end of each engagement, make and defend continue or stop recommendations to business unit and Enterprise AI leadership.
• Coach business unit engineers on AI-augmented ways of working while embedded, building their capability alongside delivery.

Requirements

• Strong full-stack or AI/ML engineering experience, including production deployments.
• Strong Python and at least one of TypeScript/JavaScript, Go or a similar language.
• Strong understanding of the main process modelling approaches: mechanistic and first-principles, hybrid, data-driven and ML, and process simulation. You need enough to work with process modelers, know what each approach requires and see where AI can speed it up. Deep mechanistic modelling experience is not expected.
• Strong AI modelling skills, including model identification: choosing and calibrating the right model structure from process data, using tools such as surrogate models, AutoML and agents that assist modelers.
• Proven record as a forward deployed engineer taking a digital twin or optimization MVP end to end, from framing the problem to a deployed, commercial-ready solution, working with scientists and modelers. This role does not own an ML engineering platform.
• Strong experience in optimization and scenario planning, including linear and mixed-integer programming, constraint solvers, scheduling optimization and simulation-based what-if analysis.
• Hands-on experience with GenAI patterns such as agents, multi-agent orchestration and evaluation, plus enough MLOps and model serving to ship an MVP to production.
• Daily use of AI coding agents such as Claude Code, GitHub Copilot or Databricks AI Assistant as your main way of developing.
• Preferred: prior work as a forward deployed engineer at an AI-native company (Anthropic, Palantir, OpenAI or similar), or as a Customer Engineer or Solutions Architect at a hyperscaler.
• Preferred: experience with digital twins, surrogate or foundation models, and platform or fitness models for pharmaceutical drug substance or drug product processes.
• Preferred: experience working with data analytics, data science or process science teams to turn analytical models into shared, reusable products.
• Preferred: experience in regulated industries such as pharma, healthcare or financial services, with familiarity with GxP, GDPR and Sarbanes-Oxley (SOX).
• Preferred: a record of contributing reusable engineering patterns back to a wider team.
• Education: a bachelor's degree in Computer Science, Software Engineering or equivalent practical experience. A master's degree in Computer Science or a related field is an advantage.
• Applications close on 10 November 2026.

Conditions

UK-based hybrid role requiring at least 2 to 3 days a week onsite. Fully remote working is not available.

Based in Central London (HQ) or Stevenage. The role may start in Stevenage on a temporary basis. The company plans to move its offices to Ware, UK, and the role will then move to Ware on the company's timeline, currently proposed for early 2029.

Equal-opportunity statement: the company welcomes applicants from all backgrounds and will provide adjustments during the process on request.

Benefits

• Access to learning and development to build technical and leadership skills.
• A supportive team that values clear communication and practical problem solving.
• The work supports technology used for critical work across the company and helps improve healthcare outcomes.