Applied AI Platforms Engineer - SAP Datasphere

Applied AI****Platform Engineer II - L35

Role Overview: As anApplied AI**Platform Engineer I**I, 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 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:

• **Outcome-Driven Accountability:**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.

• **Technical Leadership and Advocacy:**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. Participate in requirement discovery with consuming teams, component design of platform services, frameworks, and the AI control plane, and their development, testing, integration, and support.

• **Engineering Craftsmanship:**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.

• **Customer-Centric Engineering:**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.

• **Incremental and Iterative Delivery:**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.

• **Cross-Functional Collaboration and Integration:**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.

• **Advanced Technical Proficiency:**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. Learn to be a role model, leveraging these techniques to optimize platform solutioning and delivery. Demonstrate understanding of the full lifecycle of platform and product development, focusing on continuous improvement and learning.

• **Domain Expertise:**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.

• **Effective Communication and Influence:**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.

• **Engagement and Collaborative Co-Creation:**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.

• 3+ years of software and platform engineering experience with most of the following: Angular, React,, 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.

• 2+ 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).

• 2+ 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, SAP HANA, ETL Tools

• 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.

• Prior software engineering experience with the understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations, and AI-augmented spec-driven development.

• Prior experience using methodologies & tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, and ArgoCD to deliver high-quality platforms and products rapidly.

Foundation Strong data engineering foundation with deep understanding of Data Ingestion, Modeling and Security. 2-3 Years Proven Experience With SAP Datasphere l SAP Datasphere administration, monitoring, transport governance l Connectivity and integration troubleshooting l SQL, data modeling fundamentals l Tenant/space administration, connectivity, monitoring, and troubleshooting in Datasphere l Role-based access control (RBAC) and security concepts (least privilege, segregation of duties) l Working knowledge of SAP BDC (Business Data Cloud) concepts and operational troubleshooting l Solid fundamentals in SQL and data warehousing concepts (dimensional modeling, transformations, performance) l 2+ years of experience with AI/ML, with last 2 years focused on GenAI as well as technologies like OpenAI, Claude, Gemini, LangChain, Agents, Vector databases, and approaches like Prompt Engineering, fine-tuning, etc.

l Proven experience in: Python, R, TensorFlow, PyTorch, Keras, Julia, ML libraries, NLP, etc.

l Proven experience with big data technologies, Angular, React, NodeJS, Python, C#, .NET Core, Java,

Golang, SQL/NoSQL.

Core Datasphere Responsibilities

l Administer SAP Datasphere tenants and landscapes (configuration, monitoring, housekeeping).

l Manage users, roles, authorizations, and security integration; enforce governance and access controls.

l Configure and operate connections (e.g., SAP S/4HANA, SAP BW/4HANA, SAP HANA, cloud sources)

and support connectivity troubleshooting.

l Support data ingestion and orchestration (scheduling, monitoring, reruns, error handling, performance

tuning).

l Assist with data modeling in Datasphere (spaces, objects, semantic models) and promote reusable data

products.

l Understand transport and change management across environments (DEV/TEST/PROD), following

release governance.

l Establish operational practices: alerts, SLAs, runbooks, and incident/problem management.

l Support data quality and reconciliation checks; collaborate on root-cause analysis for pipeline failures.

l Provide guidance to developers and analysts on Datasphere standards, naming conventions, and best

practices.

Data Engineering Skills

l Spark, Python and SQL.

l Change Data Capture (CDC) and Slowly Changing Dimensions (SCD).

l Medallion Architecture: when and how to use it.

l Security: Row Level and Column Level Security, IAM and Roles.

l Thorough understanding of how Lakehouse works.

l Data Modeling, Data Quality and CI/CD.

Good to Have

l Experience with Databricks, Snowflake or other data platforms.

l Strong preference will be given to candidates with experience in AI/ML and GenAI.

Other:

• Ability to travel 10%, on average, based on the work you do and products you build.

• Limited immigration sponsorship may be available.

Our purpose

Deloitte’s purpose is to make an impact that matters for our people, clients, and communities. At Deloitte, purpose is synonymous with how we work every day. It defines who we are. Our purpose comes through in our work with clients that enables impact and value in their organizations, as well as through our own investments, commitments, and actions across areas that help drive positive outcomes for our communities.

Our people and culture

Our inclusive culture empowers our people to be who they are, contribute their unique perspectives, and make a difference individually and collectively. It enables us to leverage different ways of thinking, ideas and perspectives, and bring more creativity and innovation to help solve our clients’ most complex challenges. This makes Deloitte one of the most rewarding places to work.

Professional development

At Deloitte, professionals have the opportunity to work with some of the best and discover what works best for them. Here, we prioritize professional growth, offering diverse learning and networking opportunities to help accelerate careers and enhance leadership skills. Our state-of-the-art DU: The Leadership Center in India, located in Hyderabad, represents a tangible symbol of our commitment to the holistic growth and development of our people. Explore DU: The Leadership Center in India.

Benefits to help you thrive

At Deloitte, we know that great people make a great organization. Our comprehensive rewards program helps us deliver a distinctly Deloitte experience that helps that empowers our professionals to thrive mentally, physically, and financially—and live their purpose. To support our professionals and their loved ones, we offer a broad range of benefits. Eligibility requirements may be based on role, tenure, type of employment and/ or other criteria. Learn more about what working at Deloitte can mean for you.

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Requisition code: 373369