AI Engineering & Platform Lead
As the AI Engineering & Platform Lead, you serve as the central technical authority for the enterprise AI capability. Reporting to the IT Department and functionally to the Head of Digital Transformation, this foundational IT leadership role is responsible for the industrialization of AI across the organization. Your objective is to establish the infrastructure, governance, and technical talent required to scale AI initiatives.
While the Digital Transformation office identifies business opportunities, defines the global strategy and manages project delivery, you own the technical execution—ensuring every solution is built on a robust enterprise platform, adheres to established engineering standards, and is managed with operational efficiency.
IT & Architecture Core: You ensure the AI ecosystem is a core component of the IT landscape, integrated with enterprise network, security, and data platform architectures
Line vs.
Functional Leadership: You provide Line Management for the Data Scientists and AI/ML Engineers overseeing their career progression, upskilling, and technical output quality
Functional Alignment: While technical staff report functionally to Project Managers for daily execution, you maintain authority over the technical frameworks and standards they utilize Responsibilities Enterprise AI Infrastructure & Architecture Platform Strategy: Own the strategic roadmap and oversee the technical health of the enterprise AI platform, primarily utilizing Azure Machine Learning or Azure Databricks
Systems Integration: Ensure the AI ecosystem is integrated with the broader IT environment, specifically regarding Network architecture, Security protocols, and Enterprise Data platforms
Architectural Design: Develop reference architectures and infrastructure patterns (covering LLMs, Computer Vision, and Predictive Analytics) to provide reusable components for AI teams Technical Governance & Standards Engineering Frameworks: Establish and enforce company-wide technical standards across the AI lifecycle, including coding practices, model versioning, and testing protocols
Compliance & Risk: Collaborate with the Security to integrate data privacy, ethical AI guardrails, and model auditing into the automated development pipelines
Technical Validation: Act as the final authority on AI deployment patterns to ensure system maintainability, security, and long-term architectural alignment Talent Stewardship & Capability Building Line Leadership: Act as the dedicated line manager for the community of Data Scientists, and AI/ML Engineers
Professional Growth: Own the career tracks, upskilling programs, and technical certifications, ensuring the talents remain at the forefront of AI advancements
Resource Allocation: Orchestrate the assignment of technical talent into cross-functional project squads, providing the necessary technical oversight to maintain quality during delivery and technically leading some initiatives Operational Excellence & Innovation Service Reliability: Lead the post-production support and maintenance of deployed AI products, ensuring high availability and performance against enterprise SLAs
FinOps & Observability: Optimize the AI compute footprint through cost-attribution models and advanced monitoring for model drift and system health
Technology Assessment: Monitor the AI ecosystem to evaluate emerging technologies and engage with research communities or vendors (e.g., Microsoft) to maintain institutional knowledge Requirements Architecture & Platform Strategy Azure Management: Significant experience in the strategic oversight of Azure ML and Databricks within a large-scale, regulated corporate environment
IT Infrastructure: Technical knowledge of the intersection between AI and IT infrastructure, specifically Networking, Security, and Data Platforms
Design Proficiency: Demonstrated ability to create reference architectures for diverse AI workloads (LLMs, RAG, Predictive Analytics)
Technical Validation: Experience serving as a technical gatekeeper or "Design Authority," reviewing solution architectures for maintainability, security, and scalability Engineering & Lifecycle Expertise SDLC for AI: Comprehensive knowledge of the end-to-end AI lifecycle, including data ingestion, automated CI/CD, and production monitoring
Software Discipline: Proficiency in software engineering best practices (unit testing, modularity, version control) as applied to AI/ML
MLOps/LLMOps Standards: Experience defining and enforcing enterprise-grade operational standards for model deployment, validation, and performance tracking
Lifecycle Support & Maintenance: Proven experience managing the post-production support, maintenance, and reliability of mission-critical AI systems Leadership & Matrix Navigation Personnel Management: Proven experience in
