Applied AI Engineer III

Applied AI Engineer III at Deloitte, focusing on building full-stack products with integrated GenAI and agentic capabilities. Responsibilities include technical leadership, engineering craftsmanship, customer-centric development, and cross-functional collaboration. Requires experience with modern software engineering practices, AI/ML, and agentic applications, including hands-on GenAI experience with LLM integration.

What you'd actually do

  1. Embrace and drive a culture of accountability for customer and business outcomes—and for the cost of achieving them. Develop engineering solutions that solve complex problems with valuable outcomes, ensuring high-quality, lean designs and implementations, and owning the inference, token, and cloud cost of what you build.
  2. Serve as the technical advocate for products, ensuring code integrity, feasibility, and alignment with business and customer goals. Lead requirement analysis, component design, development, testing, integrations, and support.
  3. Maintain accountability for code-design integrity, implementation fidelity to architecture and tech stack, quality, data, and ongoing maintenance and operations. Be hands-on, self-driven, and continuously learn new approaches, languages, and frameworks. Create technical specifications, and write high-quality, supportable, scalable code ensuring all quality KPIs are met or exceeded. Demonstrate collaborative skills to work effectively with diverse teams.
  4. Develop lean engineering solutions through rapid, inexpensive experimentation to solve customer needs. Engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time.
  5. Adopt a mindset that favors action and evidence over extensive planning. Utilize a leaning-forward approach to navigate complexity and uncertainty, delivering lean, supportable, and maintainable solutions.

Skills

Required

  • Angular
  • React
  • NodeJS
  • Python
  • C#
  • .NET
  • Java
  • SQL/NoSQL
  • PyTorch
  • TensorFlow
  • LangChain
  • LangGraph
  • unit testing frameworks
  • AI/ML
  • agentic applications
  • GenAI
  • LLM integration

What the JD emphasized

  • hands-on GenAI experience across LLM integration
  • building AI/ML and agentic applications
  • owning the inference, token, and cloud cost of what you build

Other signals

  • building GenAI and agentic capabilities directly into the products
  • hands-on GenAI experience across LLM integration
  • building AI/ML and agentic applications