VPII, Software Engineering Manager & AI Lead - M&A & Partner Integration

We're looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.

- AI/ML and engineering leadership: 10 or more years of progressive software, AI, ML, platform, or data-intensive engineering experience, including 5 or more years in AI/ML or platform technical leadership and 3 or more years directly leading engineering teams.

- Production LLM/GenAI ownership: Experience owning production LLM/GenAI systems, including prompt and evaluation pipelines, LLM validation at scale, and output quality/cost gating on AWS Bedrock or an equivalent foundation-model platform.

- Roadmap and delivery at scale: Experience owning a technical roadmap and deliver across teams in a large-scale or regulated program, including systems operating at portfolio scale and delivery against hard deadlines.

- Static analysis and automated remediation: Experience leading large-scale code analysis, automated remediation, developer tooling, or similar engineering productivity programs with the depth to review code, prompts, and architecture decisions.

- Education: Bachelor's degree in Computer Science , Engineering, or a related field, or equivalent practical experience; Master's degree preferred.

- Hands-on technical leadership: Earns credibility through sound technical judgment while developing the team to operate with increasing independence.

- Systems thinking and prioritization: Optimizes the full scanning, validation, remediation, and handoff pipeline while focusing scarce SME and engineering capacity on must-fix work.

- Decisive executive communication: Makes evidence-based decisions on NF rules and prompt changes, then communicates quality, cost, throughput, and risk clearly to senior stakeholders.

- Experience operationalizing AI in a regulated financial-services or other compliance-driven environment.

- Familiarity with AWS-native ML/data infrastructure such as Bedrock, EKS, Neptune, S3, Step Functions, and infrastructure-as-code practices.

- Background in multi-tenancy, platform consolidation, or large-scale application-modernization programs.