Overview

The AI Engineering Manager will build and lead a high-performing AI Engineering team responsible for translating high-value scientific and business opportunities into trusted, scalable and reusable AI solutions across Biometrics and Clinical Evidence (BCE).

Responsibilities

• Build and lead the AI Engineering capability
• Build, develop and lead a high-performing team of AI Engineers, establishing a strong engineering culture focused on technical excellence, experimentation, reuse, collaboration and measurable impact.
• Define the capabilities, roles, development pathways and engineering practices required for AI Engineering across BCE and the broader R&D RDT Org..
• Translate scientific opportunities into scalable AI solutions
• Partner closely with AI Scientists, Product Managers, data teams and PD stakeholders to translate high-value business and scientific problems into technically robust AI solutions.
• Provide technical leadership from early exploration and proof of concept through architecture, development, deployment, adoption and lifecycle management.
• Ensure rapid experimentation can transition effectively into production-grade solutions where value is demonstrated.
• Establish reusable AI and agentic engineering capabilities
• Define and implement reusable patterns, components and reference architectures for generative AI, retrieval-augmented generation, agentic workflows, knowledge retrieval and AI-enabled applications.
• Drive a "build once, reuse many" engineering philosophy, reducing duplication and accelerating delivery across use cases.
• Identify common capabilities emerging across individual solutions and partner with platform teams to industrialise them as shared services where appropriate.
• Build high-performance AI context and semantic retrieval systems
• Architect scalable data pipelines, high-throughput vector search indexes, and hybrid retrieval systems explicitly tuned for LLM grounding and retrieval-augmented generation.
• Define and implement structured schema metadata formats and function-calling protocols to ensure precise, reliable agent tool invocation.
• Optimize dynamic context windows, chunking strategies, and data freshness to drive accurate model reasoning across complex enterprise domains.
• Team Member Capability and Career Development 🚀
• Proactively identifies and develops future-focused engineering team capabilities, aligning with long-term strategic goals.
• Mentors team members on long-term career paths, creating new opportunities and roles within the organization to support their growth.
• Addresses and helps resolve complex, systemic roadblocks that impact multiple development teams, and coaches other managers on effective performance management.
• Oversees the technical and soft skill development, ensuring a balanced and high-performing talent portfolio.
• Team Health and Sustainability 🌱
• Establishes and monitors metrics for team health and sustainability across several development teams, implementing strategies to ensure long-term balance.
• Designs and implements initiatives to improve collaboration and psychological safety across multiple related development teams or a whole product area.
• Proactively identifies and resolves systemic impediments that affect multiple development teams, improving the overall work environment.
• Manages the talent composition for own and related teams, planning for future needs and ensuring a healthy pipeline of skills.

Qualifications

• 8–12+ years relevant technology/software/data experience, with meaningful technical leadership experience with a proven track record of building high performing teams
• 2+ years of experience in the pharmaceutical, biotechnology, or regulated healthcare technology industry

Education

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Computational Science, Bioinformatics or a related quantitative discipline.
• Advanced degree preferred but not required where equivalent experience demonstrates deep technical capability.
• Strong practical understanding of modern AI engineering, particularly: Generative AI / LLMs • RAG • agentic architectures • tool use/function calling • orchestration • evaluation • prompt/context engineering • embeddings/vector search • knowledge retrieval • model selection • observability • Fine-tuning/Distillation, Multimodal, High-throughput serving (vLLM)
• Python • APIs • microservices • cloud architectures • CI/CD • containers • Git • automated testing • infrastructure/platform patterns • identity/access management • observability • DevSecOps • AI FinOps (Token/GPU cost management), Model fallback routing • cloud-native container orchestrations (Kubernetes, AWS/GCP serverless platforms)
• Data pipelines • semantic models • metadata • APIs • structured/unstructured data • vector databases • knowledge graphs • interoperability • data quality • lineage • provenance
• Privacy • security • GxP considerations • validation • auditability • traceability • human-in-the-loop • model evaluation • AI risk management
• Architecture standards • engineering quality • code review • reusable patterns • technical mentoring • hiring • capability development • prioritisation • delivery discipline • Vendor lock-in mitigation, Buy vs. Build frameworks, AI Change Management
• Problem Framing and Product thinking
• Scientific Curiosity and Value Orientation
• Exhibits servant and creative leadership (VACC) behavior
• Demonstrated experience building high performing teams
• Have a keen IT market focus: you are passionate about major trends in AI
• Demonstrates an executive presence, strong business acumen, ability to navigate ambiguity, and manages complexity
• Manages risks progressively and in a way that leads to opportunity
• Enterprise mindset that can break down silos. Focus on delivery through collaboration, and bringing people together to work towards the same purpose across organizational boundaries.
• Ability to empower teams and individuals to act autonomously and hold them accountable.
• Exhibits intellectual curiosity and integrity and has a strong passion for innovation