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Posted: 10/19/2026Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading AI/ML initiatives, managing cross-functional teams, and delivering complex technology solutions while ensuring alignment with business outcomes and stakeholder expectations. Proficient in AI governance, responsible AI practices, and the ML/LLM lifecycle.
Highest-signal resume keywords
AI/ML DeliveryPeople ManagementAgile/Lean PracticesStakeholder ManagementBudget Management
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Directly manage, coach, and develop a small to mid-sized team
- Lead end-to-end delivery of AI/ML, GenAI, and agentic AI initiatives from discovery and proof of concept through production and adoption
- Lead delivery across multiple initiatives and scrum teams, ensuring priorities, commitments, and business outcomes are achieved
- Partner with Product Owners, technical teams, and business stakeholders to establish priorities, success measures, delivery milestones, and production readiness
- Identify and resolve delivery risks and dependencies across data, engineering, platform, security, architecture, and business teams
- Provide technical leadership to challenge estimates, identify risks, and facilitate decisions around AI/ML delivery, evaluation, integration, and deployment
- Establish delivery standards, stage gates, and responsible AI practices throughout the lifecycle
- Manage staffing, capacity, budgets, forecasting, resource allocation, and vendor relationships across the portfolio
- Build team capability through coaching, development, cross-training, and identification of emerging skill needs
- Measure performance using delivery, adoption, quality, cost, and business outcomes and continuously improve execution
- Build trusted relationships with business and technical stakeholders and communicate risks, trade-offs, progress, and outcomes to senior leadership
- Stay current on relevant AI capabilities and delivery practices, including GenAI, RAG, agentic AI, model evaluation, and AI governance
Requirements
What you’ll need- Bachelor’s degree in a related field preferred, or equivalent experience
- 5+ years of technology delivery experience leading complex, cross-functional initiatives
- People management experience, including coaching, developing, and building high-performing technical teams
- Experience delivering AI/ML, GenAI, MLOps, ML pipeline, or data-platform solutions, with meaningful AI/ML delivery exposure
- Working knowledge of the ML/LLM lifecycle, including model evaluation, deployment, monitoring, and production support
- Technical depth to engage with data scientists and engineers and understand RAG, model evaluation, integration, and production readiness
- Experience managing budgets, forecasting, staffing, and resource allocation
- Strong stakeholder management and communication skills, including translating technical concepts for non-technical audiences
- Experience with Agile/Lean delivery practices and leading through ambiguity and changing priorities
- Familiarity with agentic AI, cloud AI platforms, and responsible AI practices preferred
Benefits
Comp & perks- Onsite fitness facilities
- Generous paid time off
- Employee professional development programs
- Healthcare benefits (health, vision, dental)
- Insurance benefits (short-term and long-term disability)
- Performance-based incentive plans
- 401(k) retirement plan with employer match up to 5%
- Additional 4.5% retirement plan contribution whether the employee contributes or not
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI/MLGenAIMLOpsML PipelineModel EvaluationDeploymentMonitoringProduction SupportData-Platform SolutionsTechnical Leadership
Soft Skills
CoachingCommunicationRelationship BuildingProblem SolvingAdaptability
Tools & Technologies
Cloud AI PlatformsAgile ToolsScrum FrameworkData Engineering ToolsAI Governance Tools
Industry Keywords
Responsible AI PracticesCross-Functional InitiativesDelivery StandardsStage GatesRisk Management