Associate Director, Applied AI Platform Engineering (PxE Platforms)
About The Position
The Associate Director, Applied AI Platform Engineering sets the engineering vision and technical direction for Deloitte’s Applied AI engineering platform, including the frameworks, tooling, control plane, and data platform used by engineering teams. The role treats the platform as a product and leads across teams and product groups, remaining hands-on in architecture, design, and code while delivering reusable components, golden paths, and governed self-service capabilities. The position aims to increase reuse and engineering leverage, reduce team-level toil, and advance Applied AI platform engineering across the organization. The role defines and drives platform strategy, roadmaps, KPIs, non-functional requirements, reference architectures, and engineering standards. Its platform scope includes AI and agentic capabilities, MLOps/LLMOps, model serving, retrieval and vector infrastructure, evaluation and observability, prompt management, feature stores, governance, enterprise data platforms, and CI/CD. Capabilities are delivered through the AI and Agentic SSDLC as automated, governed self-service pathways from discovery through production and operations. The Associate Director partners with engineering teams and stakeholders across product, SRE, security and risk, data governance, and infrastructure. The role sets quality, reliability, performance, scalability, and security expectations; embeds compliance and guardrails by default; and works with SRE on production standards and service-level objectives. It also mentors engineers and emerging leaders, promotes iterative and customer-centered delivery, communicates technical trade-offs, and fosters continuous improvement and a culture of reuse, quality, and innovation. The position is part of US Deloitte Technology Product Engineering, Deloitte’s internal development organization, which delivers digital solutions for businesses, service lines, and internal operations. Candidates must be commutable to one of the listed locations and work in their local office at least three days per week. Travel averages 10%.
Requirements
- Bachelor’s degree in computer science, software engineering, data science, machine learning, or a related discipline; experience is the most relevant factor.
- 10+ years of software and platform engineering experience with most of the listed technologies; Python is mandatory. Listed technologies include Angular, React, NodeJS, C#, .NET, Java, Rust, SQL/NoSQL, REST/SOAP/GraphQL, SSO/MFA, PyTorch, TensorFlow, LangChain, LangGraph, LangSmith, LangFuse, Terraform, and testing tools and frameworks including BDD, Gherkin, Cucumber, Playwright, and Selenium.
- 7+ years of experience architecting and delivering enterprise solutions and platforms on modern technology stacks and cloud-native environments, including API gateways, message brokers, queuing, workflow automation and orchestration, ETL/ELT, event streaming, real-time data processing, service mesh, FaaS, PaaS, microservices, cloud hyperscalers, AI/ML services, Kubernetes, Docker, big data, data platform tooling, CI/CD, infrastructure-as-code, and distributed systems.
- 5+ years of experience designing, building, and operating AI/ML platforms or infrastructure, including hands-on MLOps/LLMOps tooling, model serving, retrieval and vector infrastructure, and evaluation and observability instrumentation for LLM integrations.
- 2+ years of experience establishing engineering platform standards and golden paths, and actively leading, mentoring, and guiding teams in adopting and continuously improving those standards while treating the platform as a product.
- Prior experience with AI control-plane and agent-runtime patterns, including model/LLM gateways, A2A and MCP integration, agent runtimes, guardrails, policy-as-code, and multi-tenant isolation.
- Prior experience with enterprise data platform engineering, including data pipelines, self-service and data-product enablement, governance-as-code enforcement, and metadata/lineage.
- Prior software engineering experience and understanding of Business Context Diagrams, sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, code instrumentation, and AI-augmented spec-driven development.
- Prior experience using XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, and ArgoCD to deliver high-quality platforms and products rapidly.
- Ability to work in the local office at least three days per week and be located within commuting distance of one of the available locations.
- Ability to travel 10% on average, based on the work and products built.
- Excellent interpersonal and organizational skills, including the ability to handle diverse situations, complex projects, and changing priorities with passion, empathy, and care.
Responsibilities
- Set and communicate the vision and technical direction for the Applied AI engineering platform, aligning it with business and US Deloitte Technology strategy.
- Treat the platform as a product by mapping consuming teams’ needs to reusable, governed, self-service capabilities.
- Develop and communicate an integrated platform engineering strategy and implementation roadmap, including objectives, KPIs, adoption, maturity, compliance, and progress.
- Promote reuse, quality, and speed while reducing per-team build and operating toil and minimizing total cost of ownership.
- Define, measure, and drive platform KPIs and non-functional requirements for adoption, reliability, developer experience, performance, scalability, and security.
- Establish and evolve platform reference architectures, golden paths, reusable components, and capabilities across AI control planes, agent runtimes, MLOps/LLMOps, model serving, retrieval, vector infrastructure, evaluation, observability, prompt management, feature stores, governance, data platforms, and CI/CD.
- Deliver automated, governed self-service platform capabilities across the AI and Agentic SSDLC lifecycle, from discovery through production and operations.
- Remain hands-on in design, architecture, and code; review code, reduce technical debt, contribute to engineering teams’ delivery, and experiment with new technology.
- Mentor and develop engineers and emerging engineering leaders in modern platform engineering, distributed systems, AI and data infrastructure, cloud-native design, container orchestration, and infrastructure-as-code.
- Demonstrate thought leadership through internal showcases, conference speaking, publications, and R&D collaborations, including with academia.
- Deliver lean, iterative platform increments, validating adoption and value while keeping capabilities supportable, maintainable, and aligned with team and business goals.
- Engage platform-consuming engineering teams before, during, and after delivery to identify needs, test solutions, and encourage adoption without overengineering.
- Continuously identify platform and operating-model inefficiencies and opportunities for innovation, and guide the organization toward lean, adaptable practices.
- Establish and evolve enterprise technology-stack conformance baselines, platform standards, and quality benchmarks.
- Embed consistency, compliance, security, guardrails, and multi-tenant isolation by default; build operable, instrumented, performant, and drift-resistant capabilities.
- Work with SRE to meet production standards and co-define service-level objectives, with SRE owning admission decisions.
- Identify technical risks and develop mitigation strategies through proactive problem-solving and contingency planning.
- Influence decisions through clear written and verbal communication, structured arguments, and evidence-based technical trade-offs.
- Collaborate with stakeholders at all levels and across engineering, SRE, security and risk, and data governance to incorporate constraints into platform pathways.
Benefits
- May be eligible to participate in a discretionary annual incentive program, subject to program rules and individual and organizational performance.
- Employees are offered a broad range of benefits; specific benefits are not listed in the posting.
- Professional development opportunities include on-the-job learning, formal development programs, leadership opportunities, and mentorship.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
