Engineer II - Forward Deployed Engineer
Glaxosmithkline
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Job summary
GSK Enterprise AI seeks an Engineer II to lead BU engagements from problem framing to working prototype, at days-to-weeks cadence, with minimal supervision.
You will mentor G8 FDEs, lead small multi-FDE engagements, and advance AI-native engineering using agent workflows. You’ll ensure high-quality handoffs to the Solutions Engineer and contribute to governance and best practices.
Qualifications
- Requires a Bachelor’s degree or higher in CS/SE or equivalent.
- 3+ years of full-stack or AI engineering experience with production deployments.
- Experience with Python and other languages like TypeScript/JS/Go.
Responsibilities
- This role owns BU engagements from framing to working prototype, on days-to-weeks cadence.
- Mentor G8 FDEs and lead small multi-FDE engagements.
- Operate AI agent workflows and validate AI-generated code at engineering speed.
- Package proven use cases for the Solutions Engineer with clear tech rationale.
Skills
Education
Tools
Job description
GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver 40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems. Our diverse portfolio consists of vaccines, specialty medicines, and general medicines. Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.
GSK's Enterprise AI team builds AI solutions that prove value fast and scale to production. The Engineer II is a senior individual contributor who takes full ownership of FDE engagements end-to-end - embedding inside business unit teams, prototyping AI ideas at a days-to-weeks cadence, leading G8 FDEs on multi-engineer engagements, and handing proven solutions off cleanly to the Solutions Engineer.
This role operates at the intersection of technical depth and stakeholder influence. The role does not just execute engagements - they help qualify which BU problems are worth tackling, shape engagement standards, and actively mentor the G8 FDEs alongside them. Most prototyping is AI-augmented: AI coding assistants and multi-agent workflows handle the bulk of routine coding, freeing the Senior FDE to focus on framing, judgement, and validation. This role does not carry use cases through to long-term operations - clean handoff to the Solutions Engineer is part of how the function works.
Enterprise AI needs engineers who can own substantive BU engagements independently, guide more junior colleagues through the FDE practice, and help shape how the function qualifies and runs its work. The role is the backbone of the FDE team's delivery capacity.
Owns complex engagements end-to-end from problem framing through working prototype, at days-to-weeks cadence, with minimal supervision.
Leads small multi-FDE engagements - directing and mentoring G8 FDEs during delivery while maintaining accountability for outcome quality.
Helps shape the FDE intake pipeline - qualifying which BU problems merit engagement and challenging unclear or underspecified asks before work begins.
Owns the quality bar for pattern library contributions - ensuring reusable assets are adopted, not just submitted.
This role will provide you the opportunity to lead key activities to progress your career. These responsibilities include some of the following:
- Take full ownership of BU use cases from problem framing through to working prototype, at days-to-weeks cadence, without close supervision.
- Qualify incoming BU asks - challenge underspecified problems, assess feasibility, and define scope before committing to an engagement.
- Embed with BU teams - product owners, engineering leads, and the wider BU - building trusted working relationships and genuine domain understanding.
- Form and defend kill/continue recommendations at the end of each engagement based on evidence - presenting clearly to BU and Enterprise AI leadership.
- Ship first production versions on engagements where the path is clear and complexity is bounded, before handing forward to the Solutions Engineer.
- Lead small multi-FDE engagements - direct the work of G8 FDEs, review their output, and ensure delivery quality without becoming the bottleneck.
- Actively mentor G8 FDEs during engagements: provide technical guidance, share engagement judgement, and give structured feedback that accelerates their development.
- Contribute to FDE interview and onboarding processes where relevant.
- Operate AI agent workflows for autonomous prototype delivery - orchestrating multi-agent systems as the primary engineering mode.
- Use AI coding assistants as the default mode of building; direct, validate, and course-correct AI-generated code at engineering speed. Go beyond vibe coding: design and build agentic workflows that automate delivery end-to-end, and run delivery bot meshes as part of day-to-day engineering, guiding G8 FDEs to do the same.
- Design and orchestrate multi-agent systems end-to-end; judge when agentic approaches add value versus simpler alternatives, and guide G8 FDEs in making the same calls.
- Package proven use cases cleanly for the Solutions Engineer - working code, architecture rationale, open risks, and scaling concerns.
- Own the quality of FDE pattern library contributions - distil every engagement into reusable agents, prompt templates, and reference architectures, and ensure the bar is high enough that others can adopt without rework.
- Initiate Code Orange, Responsible AI, and Accountability Report conversations from the prototype stage - so the productionisation team does not inherit governance debt.
- Coach BU engineers on AI-augmented ways of working while embedded, leaving teams measurably more capable.
- Contribute to defining FDE engagement standards - bringing patterns and lessons from engagements back to the team in a form others can act on.
We are seeking professionals with the following required skills and qualifications to help us achieve our goals:
- Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience. Master’s degree in Computer Science or related field is advantageous.
- 3+ years of experience in full-stack software engineering, AI engineering, machine learning engineering, or a related field, including experience deploying software or AI/ML solutions to production.
- Experience programming in Python and at least one additional programming language, such as TypeScript, JavaScript, Go, or a similar language.
- Experience developing generative AI solutions using one or more of the following: agents, multi-agent orchestration, retrieval-augmented generation (RAG), prompt engineering, or evaluation.
- Experience designing, training, evaluating, or tuning machine learning or deep-learning models using frameworks such as PyTorch or TensorFlow.
- Experience with statistical analysis, experimental design, or building data pipelines using platforms such as Databricks or Apache Spark.
- Experience using AI-assisted software development tools, such as Claude Code, GitHub Copilot, Databricks AI Assistant, or comparable tools.
- Experience building agentic workflows, multi-agent systems, or software delivery automation workflows.
- Experience guiding or mentoring engineers, either through formal people leadership, technical mentorship, or leading delivery work.
If you have the following characteristics, it would be a plus:
- Prior experience as a Forward Deployed Engineer, Customer Engineer, or Solutions Architect - ideally at an AI-native company (Anthropic, Palantir, OpenAI) or hyperscaler.
- Experience in regulated industries (pharma, healthcare, financial services) and familiarity with GxP, GDPR, and SOX.
- Working knowledge of fine-tuning fundamentals and Model Context Protocol (MCP) integrations.
- Track record of contributing reusable engineering patterns that others have adopted.
Enterprise AI is GSK's centre of gravity for applied artificial intelligence. We pair forward deployed engineers and full-stack engineers with scientific, clinical, and business teams to build AI products that get used - not pilots that get archived. We work fast, ship in production, and measure ourselves on outcomes that matter to patients.
AI Frameworks, Computer Programming, Data Preprocessing, Deep Learning, Google Cloud Platform (GCP) for Machine Learning, Machine Learning Algorithms, Microsoft Azure Data and AI, ML Frameworks, Model Evaluation, Model Training, Model Validation* If you are based in Cambridge, MA; Waltham, MA; Rockville, MD; or San Francisco, CA, the annual base salary for new hires in this position ranges $131,175 to $218,625. The US salary ranges take into account a number of factors including work location within the US market, the candidate's skills, experience, education level and the market rate for the role. In addition, this position offers an annual bonus and eligibility to participate in our share based long term incentive program which is dependent on the level of the role. Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave. If salary ranges are not displayed in the job posting for a specific country, the relevant compensation will be discussed during the recruitment process. Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.
Uniting science, technology and talent to get ahead of disease together.
GSK is a global biopharma company with a purpose to unite science, technology, and talent to get ahead of disease together. We are committed to building a diverse and inclusive team - we welcome candidates from all backgrounds and underrepresented groups in technology, science, and pharma.
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