Description
A leading global pharmaceutical company is seeking an AI solutions architect who is responsible for designing, governing, delivering, and evolving the enterprise AI technical architecture across the company. Acting as the technical authority and delivery lead for AI within the AI Centre of Excellence, this role defines architectural standards, evaluates and governs AI platforms and solutions, prioritising off-the-shelf and vendor-led approaches, and ensures that all AI initiatives are scalable, secure, compliant, and aligned with the company's digital transformation strategy. The AI solutions architect works closely with AI engineers, IT business partners, AI champions, and business function leaders to assess technical feasibility, lead end-to-end solution delivery, support AI proof of value (POV) execution, and translate strategic AI ambitions into sound, future-proof solutions, ensuring every initiative is delivered on a technically rigorous and governance-compliant foundation.
Responsibilities:
- Strategy & architecture leadership
Define and own the enterprise AI technical architecture framework, including reference architectures, design patterns, integration blueprints, and technology standards across cloud and on-premises environments.
Collaborate with the architecture advisory board to ensure the AI architecture roadmap is aligned with the company's overall digital transformation strategy and enterprise IT architecture principles.
Evaluate emerging AI technologies, frameworks, and platforms, providing technical direction and recommendations to senior IT and business stakeholders.
Lead architectural governance for all AI initiatives, ensuring solutions adhere to approved standards, security requirements, regulatory constraints, and scalability principles.
Define and maintain the enterprise AI technology stack, including cloud AI services (Azure, AWS, GCP), MLOps platforms, data platforms, and integration middleware.
- AI solution design & technical oversight
Architect end-to-end AI solutions across the company's key business domains including manufacturing, quality & regulatory affairs, R&D, commercial, supply chain, HR, and finance.
Produce high-quality architecture deliverables including solution design documents, architecture decision records (ADRs), technical specifications, data flow diagrams, and integration architecture blueprints.
Lead technical design reviews and architecture assessments for all AI initiatives in the portfolio, ensuring fitness for purpose, scalability, and compliance.
Define AI integration patterns with enterprise systems including SAP, MES, LIMS, CRM, and M365 ecosystems, ensuring seamless interoperability.
Guide AI developers in translating architecture designs into well-structured, maintainable, and production-ready solutions.
Oversee the design of MLOps pipelines including model training, validation, deployment, monitoring, and retraining workflows.
- Platform engineering & infrastructure
Own the architecture and governance of enterprise AI platforms including Microsoft Azure AI, Azure Machine Learning, Microsoft 365 Copilot, and other approved AI tooling.
Define cloud infrastructure architecture for AI workloads including compute, storage, networking, and security configurations aligned with the company IT standards.
Establish standards for model lifecycle management including versioning, registry, performance monitoring, drift detection, and retraining triggers.
Drive the design of data architecture components critical to AI including feature stores, data lakes, vector databases, and real-time data pipelines in collaboration with the Data & Analytics team.
Ensure AI platforms meet GxP validation, 21 CFR Part 11, and audit trail requirements where applicable.
- Governance, compliance & responsible AI
Embed regulatory and compliance requirements, including FDA AI/ML guidance, EMA requirements, GxP, GDPR, and HIPAA, into AI architecture design and review processes.
Define and enforce responsible AI architectural guardrails including model explainability, bias detection, fairness assessments, and human-in-the-loop design patterns.
Maintain AI architecture governance documentation including standards, patterns, approved toolsets, and deviation processes within the enterprise AI knowledge repository.
Coordinate with IT security, data privacy, legal, quality assurance, and regulatory affairs to ensure AI solutions meet all applicable oversight requirements.
Support E-AIAB governance processes by providing technical input into initiative assessments, vendor evaluations, and POV planning.
- Technical leadership & enablement
Provide technical mentorship, code and architecture reviews, and hands-on guidance to the AI developer team.
Define engineering best practices, coding standards, and DevOps/MLOps conventions for the AI team.
Collaborate with external vendors, implementation partners, and cloud providers to assess solutions, conduct technical due diligence, and ensure delivery quality.
Contribute technical expertise to vendor RFP/RFI processes, proof-of-concept evaluations, and contract assessments.
Represent the company's AI technical standards in cross-functional project delivery teams and steering committees.
- Stakeholder engagement & communication
Translate complex technical architecture concepts into clear, accessible language for business stakeholders, executive leadership, and non-technical audiences.
Serve as the primary technical escalation point for AI platform issues, architecture deviations, and integration challenges.
Collaborate with IT business partners and AI champions to provide technical feasibility input into AI opportunity assessments.
Participate in external pharmaceutical AI forums, cloud provider events, and technology conferences to maintain leading-edge awareness and contribute to the company's technical reputation.
Requirements
Bachelor's degree in computer science, information technology, software engineering, data science, or related technical field.
Master's degree in artificial intelligence, data science, computer science, or related discipline is preferred.
Microsoft Azure Solutions Architect Expert, Azure AI Engineer Associate, or equivalent cloud architecture certification is preferred.
TOGAF or equivalent enterprise architecture certification is preferred.
7–10 years of professional experience in IT, software engineering, data & analytics, or AI/ML implementation.
4–6 years of hands-on experience designing and delivering AI/ML solutions on cloud platforms (Azure, AWS, or GCP) in a production environment.
Proven track record of owning end-to-end AI solution architecture in a complex, cross-functional enterprise environment.
Experience with Microsoft Azure AI, Azure Machine Learning, and Microsoft 365 Copilot architecture and deployment is preferred.
Pharmaceutical, healthcare, life sciences, or other regulated industry experience is preferred.
Experience with GxP validation, 21 CFR Part 11, or regulatory technology compliance in an AI/ML context is preferred.
Skills:
Technical competencies:
AI/ML architecture & solution design - cloud platform architecture (Azure / AWS / GCP) - ML ops & model lifecycle management - enterprise integration & API design - data architecture & data engineering - AI governance, ethics & responsible AI - pharmaceutical regulatory compliance (GxP, FDA, EMA)
AI & technology skills:
Deep expertise in AI/ML architecture patterns, including supervised/unsupervised learning, NLP, computer vision, generative AI, and LLM-based solution design.
Strong hands-on proficiency with Azure AI services, Azure Machine Learning, MLflow, or equivalent MLOps tooling.
Solid experience designing and deploying generative AI solutions including RAG architectures, LLM orchestration (LangChain, Semantic Kernel), and enterprise copilot patterns.
Strong command of enterprise integration architecture including REST APIs, event-driven architecture, message queues, and middleware platforms.
Proficiency in cloud infrastructure design including IaC (Terraform, Bicep), containerization (Docker, Kubernetes), and CI/CD pipelines.
Strong understanding of data architecture components including data lakes, lakehouses, feature stores, and vector databases.
Working knowledge of pharmaceutical business processes including GxP operations, quality management systems, and regulatory affairs workflows (preferred).
Solid understanding of AI governance frameworks, responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security principles relevant to AI deployment.