Manager, Forward Deployed Engineer
Overview:
Please make an application promptly if you are a good match for this role due to high levels of interest. In this role, you will lead end-to-end solution architecture and drive AI-native software delivery for enterprise clients. You collaborate with cross-functional teams and executives to translate business needs into scalable, secure deployments. You mentor a pod of engineers, manage agile cycles, and steer pilots from discovery to production adoption. This position offers impact across technology strategy, client outcomes, and enterprise-scale AI deployments, within KPMG’s CTI organization. Compensation / Benefits 401(k) with matchmedical insurancedental and vision coveragedisability and life insurancepersonal well-being programs Paid Time Off and holidays
Responsibilities:
- Own architecture and technical direction for AI-native pods; establish quality bars and safety testing prior to production
- Write secure production code weekly and reinforce enterprise scalability and reliability
- Lead and mentor a high-velocity engineering pod across full lifecycle from problem framing to deployment
- Drive agile delivery in 2–6 week cycles, surface risks, and contribute reusable frameworks
- Lead discovery sessions and demonstrations with technical teams and C-suite audiences, linking performance to business outcomes
- Partner with clients and sponsors to enable feedback loops and successful transitions from deployment to adoption
- Design and implement robust testing/deployment harnesses for hybrid model configurations (LLMs and Type 1 models) in enterprise environments
- Act with integrity and uphold a respectful work environment
Key requirements:
- Seven years of hands-on experience shipping secure, end-to-end production software with data/ML/AI focus
- At least three years in a hands-on leadership role managing engineering teams; founder/startup experience a plus
- Bachelor's degree in a STEM field or eight years of equivalent experience xaygatp Experience architecting and operating scalable systems in Azure, AWS, or Google Cloud
- Familiarity with agentic frameworks, coding harnesses, and model evaluation standards
- Excellent ability to communicate AI concepts to non-technical leaders and build consensus
- Verifiable portfolio or shipped codebase
- Ability to travel domestically as neededcustomer-facing presenceability to articulate complex AI concepts to non-technical leaderscross-functional collaboration
- Python, Go, Rust, or Java programmingcloud environments (Azure, AWS, GCP)AI/ML model deployment and evaluation