Lead Applied AI Platforms Engineer I

Deloitte’s Product Engineering team seeks a hands-on lead to build reusable platforms, tooling, accelerators and frameworks that enable other teams to develop and operate AI solutions at scale. The role treats the platform as a product, with adoption, reliability and developer experience as key outcomes.

Responsibilities

• Discover engineering teams’ needs; design, develop, test, integrate and support platform services, frameworks and an AI control plane. Deliver self-service capabilities through small, adoption-validated increments.
• Maintain platform architecture and enterprise technology standards. Build reusable components and golden paths, data and policy-as-code enforcement, and OpenTelemetry-based instrumentation. Write and review scalable code, create technical specifications, manage dependencies and mentor engineers.
• Work with engineering, SRE, security, risk and data-governance partners to make platform capabilities secure, compliant and operable. Co-define service-level objectives with SRE and submit capabilities against its production standards.
• Lead cross-functional delivery, project planning, risk mitigation and stakeholder reporting. The posting also assigns SAP Payroll delivery, escalated payroll issue resolution, coordination of system enhancements and fixes, and oversight of documentation and training materials.

Required qualifications and experience:

• A bachelor’s degree in computer science, software engineering, data science, machine learning or a related discipline; the posting emphasizes experience as the most relevant factor.
• 6+ years in software and platform engineering, including mandatory Python and experience with most of the listed development and testing technologies. Also required are 3+ years designing, building and operating AI/ML platforms or infrastructure, and 3+ years in cloud-native engineering with Azure, AWS or GCP, including AI/ML services, containers, distributed systems, Databricks and platform-scale CI/CD.
• Experience building MLOps/LLMOps tooling, model serving, retrieval and vector infrastructure, and LLM evaluation and observability instrumentation. The technical-skills section also calls for deep SAP HR and payroll expertise, HR ABAP, reports, interfaces, enhancements, forms, conversions, SAP Gateway, OData and Fiori/UI5.

Additional specified experience includes at least 1 year establishing engineering standards and golden paths; AI control-plane, agent-runtime and guardrail patterns; enterprise data pipelines and governance; and practices such as DevSecOps and SRE. IDOC/ALE interfaces, Adobe Forms and selected SAP enhancement and conversion tools are identified as preferred or advantageous.