Deloitte's Encore Program seeks an Applied AI Engineer II in Philadelphia, onsite, to help build high-visibility full-stack products that leverage GenAI and agentic capabilities to deliver customer-focused outcomes. This role sits at the intersection of advanced AI, software engineering, and product delivery, requiring hands-on craftsmanship, cross-functional collaboration, and a relentless focus on value creation.
The following sections outline the responsibilities, required experience, technologies, and the team context for this opportunity.
Responsibilities
- Outcome-Driven Accountability: champion solutions that deliver measurable customer and business results while managing the costs of inference, tokens, and cloud resources.
- Technical Leadership and Advocacy: act as the technical advocate for products, ensuring code quality, feasibility, and alignment with business and customer goals; participate in requirements, design, development, testing, integrations, and support.
- Engineering Craftsmanship: own code design integrity and architecture fidelity, maintainability, and operations; stay hands-on, continuously learn new approaches, and produce high-quality, scalable code with clear technical specs.
- Customer-Centric Engineering: design lean solutions through rapid, inexpensive experiments; engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time.
- Incremental and Iterative Delivery: favor action and evidence over heavy planning to deliver lean, maintainable solutions amid complexity and uncertainty.
- Cross-Functional Collaboration and Integration: work with product management, experience, and delivery teams; integrate diverse perspectives to balance feasibility, viability, usability, and value.
- Advanced Technical Proficiency: apply modern software engineering practices and AI-augmented SDLC to deliver daily product deployments with end-to-end automation and quality checks throughout discovery to production lifecycles.
- Domain Expertise: rapidly acquire domain knowledge relevant to the business or product and translate business needs into technical specifications and code while prioritizing quality and debt payoff.
- Effective Communication and Influence: clearly articulate complex concepts, influence teammates with evidence-based trade-offs, and create narratives that align technical solutions with business goals.
- Engagement and Collaborative Co-Creation: collaborate across product engineering teams at all levels and with customers as needed to foster co-creation and shared momentum toward product goals.
Requirements
- Bachelor's degree in computer science, software engineering, data science, machine learning, or a related field; experience is a key factor.
- 3+ years of experience with most of the following: Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, plus unit testing frameworks.
- 2+ years building AI/ML and agentic applications, with hands-on GenAI experience across LLM integration (OpenAI, Anthropic, or open-source models), RAG pipelines, prompt engineering, vector databases, evaluations, and AI agent orchestration.
- 2+ years of cloud-native engineering using FaaS, PaaS, or micro-services on Azure, AWS, or GCP, including AI/ML services (Azure OpenAI, AWS Bedrock, Vertex AI) and application-level infrastructure-as-code with FinOps accountability.
- Understanding of business context diagrams, sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentation; experience with AI-augmented spec-driven development.
- Experience with methodologies and tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (LangFuse, LangSmith, or equivalent multi-agent orchestration) to deliver high-quality products rapidly.
- Ability to travel approximately 10 percent, depending on client engagements and industry needs.
- Limited immigration sponsorship may be available.
Technologies
- Angular
- React
- NodeJS
- Python
- C#
- .NET
- Java
- SQL
- NoSQL
- PyTorch
- TensorFlow
- LangChain
- LangGraph
- LangFuse
- LangSmith
- OpenAI
- Anthropic
- AWS Bedrock
- Azure OpenAI
- Vertex AI
- Azure
- AWS
- GCP
- MLflow
- ADO
- GitHub
- SonarQube
- Vector databases
The Team
US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model that focuses on value and outcomes, leveraging a progressive and responsive talent structure. As Deloitte's primary internal development arm, 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 and serves as the engine that drives Deloitte's internal initiatives.
