Manager, Genai Engineering

Manager, GenAI Engineering role at Deloitte, focusing on hands-on engineering to design, develop, and deploy GenAI solutions for customers and business value. Emphasizes technical leadership, code integrity, customer-centric development, and cross-functional collaboration within an Agile/DevSecOps framework.

What you'd actually do

  1. Embrace and drive a culture of accountability for customer and business outcomes. Develop engineering solutions that solve complex problems with valuable outcomes, ensuring high-quality, lean designs and implementations.
  2. Serve as the technical advocate for products, ensuring code integrity, feasibility, and alignment with business and customer goals. Lead requirement analysis, contributing to low-level architecture and component design, development, unit testing, integrations, and support.
  3. Maintain accountability for the integrity of code design, implementation, quality, data, and ongoing maintenance and operations. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks with significant focus on infusing AI/ML/GenAI where possible/appropriate. 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

  • GenAI & engineering craftsmanship
  • advanced proficiency across multiple programming languages and modern frameworks
  • software engineering practices and principles
  • AI/ML/GenAI
  • Agile methodologies
  • DevSecOps
  • full automation from code check-in to production
  • full lifecycle product development
  • exceptional communication skills

Nice to have

  • mentor
  • technical leadership
  • customer-centric engineering
  • incremental and iterative delivery
  • cross-functional collaboration

What the JD emphasized

  • hands-on approach
  • infusing AI/ML/GenAI where possible/appropriate
  • deliver daily product deployments

Other signals

  • GenAI Engineering
  • deliver solutions that delight customers
  • driving tangible value
  • hands-on approach
  • infusing AI/ML/GenAI where possible/appropriate
  • deliver daily product deployments using full automation