Lead Applied AI Engineer II

Lead Applied AI Engineer II at Deloitte, focusing on building and enhancing full-stack products with GenAI and agentic capabilities. The role involves technical leadership, end-to-end product delivery from concept to production, and managing costs associated with inference and cloud usage. Emphasizes outcome-driven accountability, customer-centric engineering, and cross-functional collaboration within Deloitte's internal product engineering team.

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, low-level architecture and component design, development, testing, integrations, and support.
  3. Maintain accountability for the integrity of architecture and technical stack to the enterprise standards and strategy. Manage dependencies, code design, implementation, quality, data, and ongoing maintenance and operations. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks. Create technical specifications, and write high-quality, supportable, scalable code and review code of other engineers, mentoring them, to ensure 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

  • full-stack software engineering
  • modern frameworks
  • applied AI fluency
  • GenAI capabilities
  • agentic capabilities
  • modern software engineering practices and principles
  • AI and Agentic SSDLC
  • full automation from discovery to production to operations
  • communication skills
  • technical leadership

Nice to have

  • mentoring
  • domain-specific knowledge

What the JD emphasized

  • owning the inference, token, and cloud cost
  • AI and Agentic SSDLC

Other signals

  • building GenAI and agentic capabilities directly into products
  • owning the inference, token, and cloud cost
  • AI and Agentic SSDLC to deliver daily product deployments