Applied AI Platform Engineer III

Deloitte • Jersey City, NJ

9d • $102,500 - $210,600

About The Position

As an Applied AI Platform Engineer III, you will take a hands-on role building platforms, tooling, accelerators, and frameworks used by other engineering teams. You will apply engineering expertise across platform engineering, distributed systems, and modern AI/ML and data infrastructure to deliver high-quality, reusable solutions that create leverage and value across Deloitte’s AI engineering investments. You will collaborate with cross-functional teams to build and operate the enabling layer for AI engineering at scale.

The role focuses on creating platform capabilities that solve recurring problems for multiple teams, reduce build and operational toil, and make consistency and compliance the default. You will advocate for the platform as a product, discover requirements with consuming teams, and lead the design, development, testing, integration, and support of platform services, frameworks, and the AI control plane. You will help maintain platform architecture and enterprise technology-stack standards, manage dependencies and code design, and build policy-as-code enforcement and OpenTelemetry-based instrumentation. Capabilities should be operable, instrumented, performant, and resistant to drift, while meeting production standards set by SRE.

You will work directly with engineering teams to experiment rapidly, deliver lean and iterative capabilities, validate adoption, and keep solutions supportable and maintainable. You will collaborate with engineering, SRE, security and risk, data governance, and engineering leadership and architecture to integrate their constraints into secure, compliant, and reliable paved roads. You will help define service-level objectives with SRE and submit capabilities for admission, with SRE owning production standards and admission decisions.

The position also requires building domain knowledge of the enterprise data estate and AI use cases, translating engineering, architecture, and governance needs into reusable frameworks and governance-as-code enforcement, and communicating technical concepts and trade-offs clearly. The team is US Deloitte Technology Product Engineering, Deloitte’s internal development team, which delivers digital solutions for businesses, service lines, and internal operations.

Requirements

• Bachelor’s degree in computer science, software engineering, data science, machine learning, or a related discipline; experience is identified as the most relevant factor.
• At least 5 years of software and platform engineering experience with most of the following: Angular, React, NodeJS, Python (mandatory), C#, .NET, Java, Rust, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, LangSmith, LangFuse, Terraform, and unit, integration, and end-to-end testing frameworks and tools, specifically BDD, Gherkin, Cucumber, Playwright, and Selenium.
• At least 3 years designing, building, and operating AI/ML platforms or infrastructure, including hands-on tooling for MLOps/LLMOps, model serving, retrieval and vector infrastructure, and evaluation/observability instrumentation for LLM integration with OpenAI, Anthropic, or open-source models.
• At least 3 years of cloud-native engineering on Azure, AWS, or GCP, including AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI; container orchestration with Kubernetes and Docker; Big Data; Databricks; CI/CD at platform scale; and distributed systems.
• Prior experience with AI control-plane and agent-runtime patterns, including model/LLM gateways, A2A and MCP integration, agent runtimes such as Google ADK or Amazon Bedrock AgentCore, 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 with methodologies and tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, and ArgoCD to deliver high-quality platforms and products rapidly.
• Ability to travel an average of 10%, based on the work performed 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.
• Limited immigration sponsorship may be available.

Responsibilities

• Build platforms, tooling, accelerators, and frameworks that enable engineering teams to deliver AI solutions at scale.
• Create reusable platform capabilities that reduce per-team development and operational toil while promoting consistency and compliance by default.
• Discover requirements with consuming teams and lead the design, development, testing, integration, and support of platform services, frameworks, and the AI control plane.
• Maintain platform architecture integrity and enterprise technology-stack conformance; manage platform dependencies, code design, and implementation.
• Build and maintain data and policy-as-code enforcement layers and OpenTelemetry-based instrumentation.
• Deliver operable, instrumented, performant, and drift-resistant capabilities that meet production standards set by SRE and platform KPIs for adoption, reliability-by-design, and developer experience.
• Create technical specifications and codify recurring patterns into reusable components and golden paths; write high-quality, supportable, scalable code.
• Engage with consuming engineering teams before, during, and after delivery to identify needs, deliver appropriate capabilities, and encourage adoption.
• Deliver platform capabilities incrementally through rapid experimentation and adoption validation, keeping them maintainable and supportable.
• Collaborate with engineering, SRE, security and risk, data governance, and engineering leadership and architecture to build secure, compliant, reliable, and admissible-by-design paved roads.
• Co-define service-level objectives with SRE and submit capabilities for admission.
• Develop knowledge of the enterprise data estate and AI use-case patterns; translate engineering requirements, reference architectures, and governance needs into reusable frameworks and governance-as-code enforcement.
• Communicate technical concepts and evidence-based trade-offs clearly, align solutions with business objectives, and build constructive relationships with product engineering teams and customers as needed.
• Support teammates, contribute to team collaboration and innovation, and focus on quality and technical-debt payoff.

Benefits

• Employees are offered a broad range of benefits; specific benefits are not listed in the posting.
• Opportunities to build new skills, take on leadership opportunities, and grow through mentorship and formal development programs.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level