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USI - EH27 - Product Engineering TAX - Applied AI Platform Engineer (PEGA Systems Engineer) - Software Specialist Engineer II
mid · Technology / Software Development
Applied AI Platform Engineer (PEGA Systems Engineer) - Software Specialist Engineer II
Role Overview: As an Applied AI Platform Engineer (PEGA Systems Engineer), you will actively engage in your engineering craft, taking a hands-on approach to building the platforms, tooling, accelerators, and frameworks that other engineering teams build on. Your expertise will be pivotal in delivering platform capabilities that delight the engineers who depend on them, while driving tangible leverage and value across Deloitte’s AI engineering investments. You will leverage your extensive engineering craftsmanship across platform engineering, distributed systems, and modern AI/ML and Data infrastructure, consistently demonstrating your strong track record in delivering high-quality, reusable, outcome-focused solutions. The ideal candidate will be a dependable team player, collaborating with cross-functional teams to design, build, and operate the enabling layer for AI engineering at scale.
Key Responsibilities:
- Embrace and drive a culture of accountability for engineering-leverage and adoption outcomes. Build platform capabilities that solve recurring problems once, well, for many teams—reducing per-team build and operate toil while ensuring consistency and compliance by default through high-quality, lean designs and implementations.
- Serve as the technical advocate for the platform as a product, ensuring capability integrity, feasibility, and alignment with the needs of the engineering teams who consume it. Lead requirement discovery with consuming teams, component design of platform services, frameworks, and the AI control plane, and their development, testing, integration, and support.
- Maintain accountability for the integrity of the platform architecture and for the enterprise tech-stack conformance baseline that engineering teams build against. Manage platform dependencies, code design, implementation, the data and policy-as-code enforcement layers, and the OpenTelemetry-based instrumentation substrate—building capabilities that are operable, instrumented, performant, and drift-resistant by design, to the production standards set by SRE. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks. Create technical specifications, codify recurring patterns into reusable components and golden paths, and write high-quality, supportable, scalable code to ensure all platform KPIs (adoption, reliability-by-design, and developer experience) are met or exceeded. Demonstrate collaborative skills to work effectively with diverse teams.
- Develop lean platform capabilities through rapid, inexpensive experimentation to solve the real needs of consuming engineering teams. Engage with those teams before, during, and after delivery to ensure the right capability is delivered at the right time—and adopted, not shelved.
- Adopt a mindset that favors action and evidence over extensive planning. Utilize a leaning-forward approach to navigate complexity and uncertainty, delivering platform capabilities as lean, adoption-validated increments rather than big-bang builds, and keeping them supportable and maintainable.
- Work collaboratively with empowered, cross-functional partners: engineering, SRE, security and risk, data governance, and engineering leadership and architecture. Integrate their constraints into the paved roads so that the secure, compliant, and reliable path is the easy path. Deliver capabilities that are admissible-by-design and submit them for admission, co-defining service-level objectives with SRE, who set production standards and own the admission decision. Foster a collaborative environment that enhances team synergy and innovation.
- Possess expertise in platform engineering and modern AI/ML infrastructure—internal developer platforms, MLOps/LLMOps, model serving, retrieval and vector infrastructure, eval and observability tooling, policy-as-code, container orchestration, IaC, and CI/CD at platform scale—including AI and Agentic SSDLC to deliver self-service, governed capabilities with full automation from discovery to production to operations and all quality checks through the SSDLC lifecycle. Strive to be a role model, leveraging these techniques to optimize platform solutioning and delivery. Demonstrate strong understanding of the full lifecycle of platform and product development, focusing on continuous improvement and learning.
- Quickly acquire domain knowledge of the enterprise data estate and the AI use-case patterns the platform must serve. Translate the needs of engineering teams, reference architectures, and governance requirements into reusable frameworks, the data platform, and governance-as-code enforcement. Be a valuable, flexible, and dedicated team member, supportive of teammates, and focused on quality and tech debt payoff.
- Exhibit exceptional communication skills, capable of articulating complex technical concepts clearly and compellingly. Inspire and influence teammates and product teams through well-structured arguments and trade-offs supported by evidence. Create coherent narratives that align technical solutions with business objectives.
- Engage and collaborate with product engineering teams at all organizational levels, including customers as needed. Build and maintain constructive relationships, fostering a culture of co-creation and shared momentum towards achieving product goals. Align diverse perspectives and drive consensus to create feasible solutions.
The team: US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model that focuses on value/outcomes that leverages a progressive and responsive talent structure. As Deloitte’s primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom-line results and outcomes. It helps power Deloitte’s success. It is the engine that drives Deloitte, serving many of the world’s largest, most respected companies. We develop and deploy cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence.
The successful candidate will possess:
§ Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.
Required Qualifications:
§ A bachelor’s degree in computer science, software engineering, data science, machine learning, or related discipline. Experience is the most relevant factor.
Prior experience with enterprise case management & low-code workforce platforms (PEGA) is important.
§ 6+ 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, as well as unit, integration, and end-to-end testing frameworks & tools, specifically BDD, Gherkin, Cucumber, Playwright, and Selenium.
§ 3+ years of experience designing, building, and operating AI/ML platform or infrastructure, with hands-on experience across building tooling for MLOps/LLMOps, model serving, retrieval and vector infrastructure, and eval/observability instrumentation for LLM integration (OpenAI, Anthropic, or open-source models).
§ 3+ years of experience with cloud-native engineering on any of the cloud hyperscalers such as Azure, AWS, or GCP—including their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI—as well as container orchestration (Kubernetes, Docker), Big Data, Databricks, CI/CD at platform scale, and distributed systems.
§ Prior experience with AI control-plane and agent-runtime patterns: model/LLM gateway, A2A and MCP integration, agent runtimes (e.g., Google ADK, Amazon Bedrock AgentCore), guardrails (PII redaction, prompt-injection, content, tool permissioning/tool-RBAC), policy-as-code, and multi-tenant isolation.
§ Prior experience with enterprise data platform engineering: data pipelines, self-service and data-product enablement, governance-as-code enforcement, and metadata/lineage.