Build a team culture that encourages experimentation, sound engineering judgment, accountability, and early discussion of problems.
Identify skill and capacity gaps and address them through development, hiring, or adjustments to scope.
Own team priorities and delivery
Partner with product management and technical leaders to define team outcomes, prioritize work, and maintain an achievable roadmap.
Turn ambiguous needs into clear problems, bounded initiatives, and measurable acceptance criteria.
Establish delivery plans that account for technical uncertainty, dependencies, staffing, and operational responsibilities.
Track progress through working software and evidence, communicate risks early, and adjust plans as new information emerges.
Resolve obstacles within the team’s scope and work with peer managers and leaders on issues that require broader decisions.
Establish AI-native development practices
Make effective use of AI part of the team’s everyday engineering practices, from planning and implementation through review and maintenance.
Guide adoption of spec-driven development so engineers and agents work from clear intent, constraints, and executable acceptance criteria.
Support disciplined development of agent loops, workflow graphs, and harnesses, with appropriate evaluation, recovery, and human oversight.
Establish ownership and maintenance practices for specifications, skills, prompts, context configurations, and evaluations.
Evaluate how AI changes the team’s work and improve practices based on quality, delivery outcomes, and total human effort.
Guide technical quality and operations
Partner with technical leads to make sound design decisions and maintain clear ownership of components and interfaces.
Ensure the team builds appropriate testing, evaluation, security controls, and observability into its capabilities.
Review significant technical tradeoffs and ensure unresolved architectural questions receive the right expertise.
Own the operational health of the team’s services, including deployment readiness, incident response, and follow-through on reliability improvements.
Balance feature delivery with maintenance, technical debt, execution cost, and sustainable operational workload.
Deliver useful capabilities and support adoption
Lead delivery of capabilities within the team’s assigned area, such as agent integrations, context services, reusable AI assets, evaluation tooling, or execution workflows.
Work with internal customers to understand their needs, establish pilots, and assess results in real development workflows.
Coordinate with partner teams on interfaces, dependencies, rollout, and support.
Ensure documentation, examples, and onboarding support effective adoption.
Use customer feedback and operational evidence to prioritize improvements.
Measure outcomes and improve team effectiveness
Define team measures that connect delivery to developer and customer outcomes, including task completion, quality, adoption, human effort, reliability, and cost.
Account for review, correction, and supervision effort when assessing AI-enabled workflows.
Use evidence to decide which experiments to expand, revise, or stop.
Improve planning, collaboration, and engineering practices while maintaining a sustainable pace.
Required qualifications
Experience managing a software engineering team, including hiring, coaching, performance management, and career development.
A strong software engineering background and experience delivering and operating production services or developer tools.
Demonstrated ability to translate priorities into focused team plans and deliver through technical uncertainty and cross-team dependencies.
Technical judgment sufficient to guide design discussions, assess risks, and partner effectively with senior engineers.
Practical experience applying AI to software development or delivering AI-enabled capabilities, with an understanding of evaluation and human oversight.
Familiarity with AI-native development approaches such as spec-driven development, coding agents, agent loops, or workflow graphs.
Experience establishing engineering practices for testing, security, observability, and operational reliability.
Clear communication skills and the ability to resolve ambiguity, align partners, and hold the team accountable for outcomes.
Preferred qualifications
Experience leading a team building developer platforms, internal tools, automation, or AI-enabled services.
Experience introducing AI-native engineering practices and assessing their effect on team productivity and software quality.
Familiarity with agent harnesses, MCP integrations, context retrieval, reusable skills, or distributed workflow execution.
Experience moving experimental capabilities into supported production use.
Experience partnering with internal engineering customers to drive adoption and improve developer workflows.
Familiarity with OCI or comparable cloud infrastructure and enterprise software development environments."
Career Level - M3
