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
:1. Strategy & Architecture Leadershi
- pDefine and own the enterprise AI technical architecture framework, including reference architectures, design patterns, integration blueprints, and technology standards across cloud and on-premises environment
- sCollaborate 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 principle
- sEvaluate emerging AI technologies, frameworks, and platforms, providing technical direction and recommendations to senior IT and business stakeholder
- sLead architectural governance for all AI initiatives, ensuring solutions adhere to approved standards, security requirements, regulatory constraints, and scalability principle
- sDefine and maintain the enterprise AI technology stack, including cloud AI services (Azure, AWS, GCP), MLOps platforms, data platforms, and integration middlewar
e2. AI Solution Design & Technical Oversigh
- tArchitect end-to-end AI solutions across the company's key business domains including Manufacturing, Quality & Regulatory Affairs, R&D, Commercial, Supply Chain, HR, and Financ
- eProduce high-quality architecture deliverables including solution design documents, architecture decision records (ADRs), technical specifications, data flow diagrams, and integration architecture blueprint
- sLead technical design reviews and architecture assessments for all AI initiatives in the portfolio, ensuring fitness for purpose, scalability, and complianc
- eDefine AI integration patterns with enterprise systems including SAP, MES, LIMS, CRM, and M365 ecosystems, ensuring seamless interoperabilit
- yGuide AI Developers in translating architecture designs into well-structured, maintainable, and production-ready solution
- sOversee the design of MLOps pipelines including model training, validation, deployment, monitoring, and retraining workflow
s3. Platform Engineering & Infrastructur
- eOwn the architecture and governance of enterprise AI platforms including Microsoft Azure AI, Azure Machine Learning, Microsoft 365 Copilot, and other approved AI toolin
- gDefine cloud infrastructure architecture for AI workloads including compute, storage, networking, and security configurations aligned with the company IT standard
- sEstablish standards for model lifecycle management including versioning, registry, performance monitoring, drift detection, and retraining trigger
- sDrive 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 tea
- mEnsure AI platforms meet GxP validation, 21 CFR Part 11, and audit trail requirements where applicabl
e4. Governance, Compliance & Responsible A
- IEmbed regulatory and compliance requirements, including FDA AI/ML guidance, EMA requirements, GxP, GDPR, and HIPAA, into AI architecture design and review processe
- sDefine and enforce responsible AI architectural guardrails including model explainability, bias detection, fairness assessments, and human-in-the-loop design pattern
- sMaintain AI architecture governance documentation including standards, patterns, approved toolsets, and deviation processes within the enterprise AI knowledge repositor
- yCoordinate with IT Security, Data Privacy, Legal, Quality Assurance, and Regulatory Affairs to ensure AI solutions meet all applicable oversight requirement
- sSupport E-AIAB governance processes by providing technical input into initiative assessments, vendor evaluations, and POV plannin
g5. Technical Leadership & Enablemen
- tProvide technical mentorship, code and architecture reviews, and hands-on guidance to the AI Developer tea
- mDefine engineering best practices, coding standards, and DevOps/MLOps conventions for the AI tea
- mCollaborate with external vendors, implementation partners, and cloud providers to assess solutions, conduct technical due diligence, and ensure delivery qualit
- yContribute technical expertise to vendor RFP/RFI processes, proof-of-concept evaluations, and contract assessment
- sRepresent the company's AI technical standards in cross-functional project delivery teams and steering committee
s6. Stakeholder Engagement & Communicatio
- nTranslate complex technical architecture concepts into clear, accessible language for business stakeholders, executive leadership, and non-technical audience
- sServe as the primary technical escalation point for AI platform issues, architecture deviations, and integration challenge
- sCollaborate with IT Business Partners and AI Champions to provide technical feasibility input into AI opportunity assessment
- sParticipate in external pharmaceutical AI forums, cloud provider events, and technology conferences to maintain leading-edge awareness and contribute to the company's technical reputatio
n
Requirement
- s:Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, or related technical fie
- ldA master's degree in Artificial Intelligence, Data Science, Computer Science, or related discipline is preferr
- edMicrosoft Azure Solutions Architect Expert, Azure AI Engineer Associate, or equivalent cloud architecture certification is preferr
- edTOGAF or equivalent enterprise architecture certification is preferr
- ed7–10 years of professional experience in IT, software engineering, data & analytics, or AI/ML implementati
- on4–6 years of hands-on experience designing and delivering AI/ML solutions on cloud platforms (Azure, AWS, or GCP) in a production environme
- ntProven track record of owning end-to-end AI solution architecture in a complex, cross-functional enterprise environme
- ntExperience with Microsoft Azure AI, Azure Machine Learning, and Microsoft 365 Copilot architecture and deployment is preferr
- edPharmaceutical, healthcare, life sciences, or other regulated industry experience is preferr
- edExperience with GxP validation, 21 CFR Part 11, or regulatory technology compliance in an AI/ML context is preferr
ed
Skil
ls:Technical Competenci
es: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, E
MA)
AI & Technology Ski
- lls:Deep expertise in AI/ML architecture patterns, including supervised/unsupervised learning, NLP, computer vision, generative AI, and LLM-based solution de
- signStrong hands-on proficiency with Azure AI Services, Azure Machine Learning, MLflow, or equivalent MLOps too
- lingSolid experience designing and deploying generative AI solutions including RAG architectures, LLM orchestration (LangChain, Semantic Kernel), and enterprise copilot patt
- ernsStrong command of enterprise integration architecture including REST APIs, event-driven architecture, message queues, and middleware platf
- ormsProficiency in cloud infrastructure design including IaC (Terraform, Bicep), containerization (Docker, Kubernetes), and CI/CD pipel
- inesStrong understanding of data architecture components including data lakes, lakehouses, feature stores, and vector datab
- asesWorking knowledge of pharmaceutical business processes including GxP operations, quality management systems, and regulatory affairs workflows (Prefer
- red)Solid understanding of AI governance frameworks, responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security principles relevant to AI deploy
ment
