Applied AI Platforms Engineer II

Deloitte’s Applied AI Platforms Engineer II builds and operates reusable platforms, tooling, accelerators and frameworks that enable other engineering teams to deliver AI solutions at scale. The role combines hands-on platform engineering with distributed systems, AI/ML infrastructure and enterprise data engineering.

Responsibilities

• Discover requirements with consuming teams; design, develop, test, integrate and support platform services, frameworks and an AI control plane. Deliver lean, adoption-validated capabilities that reduce duplicated engineering and operational work.
• Maintain platform architecture and enterprise technology-stack conformance. Build reusable components, technical specifications and golden paths, including data and policy-as-code enforcement and OpenTelemetry-based instrumentation. Write scalable, supportable code and monitor adoption, reliability and developer experience.
• Develop self-service, governed AI/ML capabilities spanning MLOps/LLMOps, model serving, retrieval and vector infrastructure, evaluation, observability, container orchestration, infrastructure as code and platform-scale CI/CD. Translate enterprise data, reference architecture and governance needs into reusable platform and data capabilities.
• Partner with engineering, SRE, security and risk, data governance, leadership and architecture teams. Incorporate their constraints into secure, compliant delivery paths, and co-define service-level objectives with SRE for production admission.

Required qualifications

• A bachelor’s degree in computer science, software engineering, data science, machine learning or a related discipline; experience is emphasized. At least 5 years of software and platform engineering experience, including mandatory Python and experience with most of the listed engineering languages, frameworks and testing tools.
• At least 3 years designing, building and operating AI/ML platforms or infrastructure, including MLOps/LLMOps tooling, model serving, retrieval and vector infrastructure, and evaluation or observability for LLM integration.
• The posting states both 3+ years of cloud-native engineering with AI/ML cloud services, containers, Databricks, CI/CD and distributed systems, and 5+ years using SaaS/PaaS on Azure, AWS or GCP. It also calls for AI control-plane and agent-runtime patterns, guardrails, enterprise data pipelines and governance, software design fundamentals, SQL/T-SQL, Synapse, Scala and DevSecOps practices. Strong communication, collaboration and organizational skills are required.

Preferred

An advanced degree is preferred but not required; experience in AI/ML and generative AI receives strong preference. Average travel is approximately 10%. Limited immigration sponsorship may be available.