D
Not Specified Permanent

Tulsa, Oklahoma · USA job

GenAI Applied Scientist and Solution Engineer

Deloitte

Tulsa, Oklahoma

Job description

Overview

In this role you will drive end-to-end agentic AI solutions, from intake and feasibility to prototyping and deployment. You will bridge business stakeholders and technical teams, shaping architectures and tooling choices for scalable AI outcomes. You'll apply software engineering, cloud architecture, and generative AI expertise to deliver practical, ROI-focused solutions. This is a hands-on, cross-functional role that emphasizes responsible AI, risk-aware design, and rapid value delivery at enterprise scale. Join a team accelerating the adoption of agentic AI in Deloitte's enterprise solutions.

Compensation / Benefits
  • Discretionary annual incentive program
  • Professional development opportunities
  • Inclusive culture and accommodations for people with disabilities
Responsibilities
  • Manage end-to-end intake and feasibility assessment for agentic AI solutions
  • Design solution architecture and gather requirements across business and technical teams
  • Select platforms and tools, and prototype initial implementations
  • Facilitate clear handoffs between business stakeholders and development teams
  • Evaluate build-vs-buy options, risks, ROI, and timelines
  • Coordinate multi-project delivery and mentor engineers
  • Address responsible AI concerns including privacy, auditability, and risk controls
  • Stay informed on emerging agentic frameworks and interoperability standards
Key requirements
  • Strong Python and Git proficiency
  • Cloud solution architecture experience on Azure, GCP, or AWS
  • Ability to translate business objectives into technical options and considerations
  • Hands-on experience with Generative AI APIs (OpenAI, Anthropic)
  • Experience with AI tools such as Claude and ChatGPT
  • Familiarity with agentic frameworks (LangChain, LangGraph, Vertex AI Agent Development Kit, OpenAI Agents SDK)
  • Ability to evaluate build-vs-buy decisions, data needs, ROI, and timelines
  • Experience collaborating with stakeholders to align opportunities and communicate trade-offs
  • Experience in project/scoped delivery and mentoring engineers
  • Familiarity with Responsible AI considerations and enterprise risk management
  • Preferred: exposure to MCP and A2A standards for agent interoperability
  • Preferred: full lifecycle experience from intake to deployment
  • Willingness to travel 0-10%
  • Legal authorization to work in the United States without sponsorship
  • Analytical and problem-solving mindset
  • Strong stakeholder collaboration and communication
  • Leadership and mentoring capability
  • Python
  • Git
  • CI/CD

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