AI Software Architect - RDT Biometrics and Clinical Evidence

Description

The team is building and leading a Data & AI Engineering group within Biometrics and Clinical Evidence (BCE) that turns high-value scientific and business opportunities into trusted, scalable, reusable Data & AI solutions.

You will work closely with data and product teams, combining deep drug development knowledge with modern Data & AI engineering to take solutions from experiments and proofs of concept through to production and ongoing use.

The team sets engineering practices and shared capabilities so that Data & AI and agentic solutions can be delivered faster, safely and at scale, meeting the company's standards for security, privacy, quality, governance and regulatory compliance.

This lead role covers the full AI stack, from front-end user experience and back-end services to data and context provisioning and the AI engine with agentic workflows.

BCE is central to drug development. It helps improve trial design and protocols, reduces risk through simulation, and manages high-quality clinical trial, real-world, imaging and patient-centric data to generate evidence and support regulatory filings.

The company employs more than 100,000 people worldwide and works on advancing science and healthcare.

Responsibilities

• Build and lead a team of Data & AI Engineers with a focus on technical quality, experimentation, reuse, collaboration and measurable results.
• Define the capabilities, roles, career paths and engineering practices for AI Engineering across BCE and the wider R&D RDT organisation.
• Work with product managers, data teams and PD stakeholders to turn business and scientific problems into sound AI solutions.
• Provide technical leadership from early exploration and proof of concept through architecture, build, deployment, adoption and lifecycle management.
• Make sure promising experiments can move into production-grade solutions once their value is shown.
• Design and build reusable patterns, components and reference architectures for generative AI, retrieval-augmented generation, agentic workflows, knowledge retrieval and AI-enabled applications.
• Reduce duplicated work by promoting reuse across use cases, and work with platform teams to turn shared capabilities into common services.
• Design scalable data pipelines, fast vector search indexes and hybrid retrieval systems tuned for LLM grounding and retrieval-augmented generation.
• Define structured schema metadata formats and function-calling protocols so agents invoke tools precisely and reliably.
• Tune context windows, chunking strategies and data freshness to improve model reasoning over complex enterprise domains.
• Identify future engineering capabilities the team will need and build them in line with long-term goals.
• Mentor team members on long-term career paths and create new roles and opportunities within the organisation.
• Help resolve complex, systemic blockers affecting several development teams, and coach other managers on performance management.
• Guide technical and interpersonal skill development to keep the talent mix balanced and strong.
• Set and track team health and sustainability metrics across several development teams, and put plans in place to keep them balanced.
• Launch initiatives that improve collaboration and psychological safety across related teams or a whole product area.
• Find and remove systemic obstacles that slow several teams down.
• Plan the talent mix for your team and related teams, and maintain a healthy pipeline of skills.

Requirements

• 8 to 12+ years of experience in technology, software or data, including meaningful technical leadership and a record of building high-performing teams.
• At least 2 years working in pharmaceuticals, biotechnology or regulated healthcare technology.
• A bachelor's or master's degree in computer science, software engineering, data science, artificial intelligence, computational science, bioinformatics or a related quantitative field.
• An advanced degree is preferred but not required if equivalent experience shows deep technical ability.
• Practical knowledge of modern AI engineering, including generative AI and LLMs, RAG, agentic architectures, tool use and function calling, orchestration, evaluation, prompt and context engineering, embeddings and vector search, knowledge retrieval, model selection, observability, fine-tuning and distillation, multimodal models, and high-throughput serving with vLLM.
• Hands-on skill in Python, APIs, microservices, cloud architectures, CI/CD, containers, Git, automated testing, infrastructure and platform patterns, identity and access management, observability, DevSecOps, AI FinOps (token and GPU cost management), model fallback routing, and cloud-native orchestration with Kubernetes or AWS/GCP serverless platforms.
• Experience with data pipelines, semantic models, metadata, APIs, structured and unstructured data, vector databases, knowledge graphs, interoperability, data quality, lineage and provenance.
• Understanding of privacy, security, GxP, validation, auditability, traceability, human-in-the-loop review, model evaluation and AI risk management.
• Experience with architecture standards, engineering quality, code review, reusable patterns, technical mentoring, hiring, capability development, prioritisation and delivery discipline, plus vendor lock-in mitigation, buy-versus-build frameworks and AI change management.
• Strong product thinking and the ability to frame problems clearly.
• Scientific curiosity and a focus on value.
• Proven ability to build high-performing teams.
• Keen interest in major AI trends in the IT market.
• Executive presence, strong business judgement, comfort with ambiguity, and the ability to manage complexity.
• Ability to take on risks step by step in ways that open up opportunities.
• An enterprise mindset that breaks down silos and brings people together across organisational boundaries.
• Ability to empower teams and individuals to act independently while holding them accountable.
• Intellectual curiosity, integrity and a real drive for innovation.

Conditions

Salary range for this role in Warsaw Grafit is 228,900 PLN to 425,100 PLN. The final figure depends on skills, experience, qualifications and location.

A discretionary annual bonus with a 20% target may apply, based on individual and company performance.

Office-based role at the Warsaw Grafit location, as the primary location is Warsaw.

The company is an equal opportunity employer.

Benefits

• Salary range of 19,075 to 35,425 PLN gross, depending on the employment contract.
• Annual performance bonus.
• Training budget for courses, certifications, conferences and alternative career paths.
• Recharge Fridays, with 2 Fridays off per quarter available.
• Take Time Program, offering up to 3 months of leave for any purpose.
• Vacation subsidy.
• Flex Location, allowing you to work from different places in the world for a set period.
• Take Time for Charity, with up to 2 weeks of additional paid leave for a charity of your choice.
• Private healthcare through LuxMed packages, group life insurance through UNUM, and Multisport.
• Contributions toward buying company shares.
• Yearly opportunity to buy company laptops and cars, plus other perks.