This role is responsible for driving the end-to-end strategy, governance, and execution of the AI Software Development Lifecycle (AI SDLC) across the organization. The Senior Manager Dev exp & AI SDLC Program provides single-threaded program leadership to define, standardize, and operationalize tools, platforms, processes, and metrics that enable secure, scalable, compliant, and high-quality AI-enabled software delivery.
The role partners closely with engineering, data science, MLOps, security, legal/compliance, product management, and client engagement teams to ensure AI initiatives move efficiently from ideation to production and sustained operations. The role leads cross-functional programs rather than day-to-day DevOps execution, focusing on strategy, alignment, and outcomes across multiple teams and products.
Responsibilities
Program & Strategy Leadership
- Owns the E2E AI SDLC program, spanning data ingestion, model development, validation, deployment, monitoring, and continuous improvement.
- Defines and drives DevX strategy for AI and software delivery, aligning engineering execution with business, security, and compliance objectives.
- Leads cross-org initiatives to standardize AI SDLC practices, including MLOps, CI/CD for AI, model governance, observability, and release readiness.
- Translates executive priorities into clear program roadmaps, milestones, and success metrics.
Technical Governance & Enablement
- Establishes reference architectures, standards, and guardrails for AI-enabled systems across cloud and hybrid environments.
- Partners with DevOps, MLOps, platform, and security teams to ensure tooling alignment across CI/CD, model lifecycle management, monitoring, and access control.
- Ensures security, privacy, Responsible AI, and regulatory requirements are embedded into the AI SDLC by design.
- Evaluates emerging technologies and platforms to recommend scalable, future-ready solutions for AI and developer productivity.
Execution Oversight (Not Line Execution)
- Oversees execution of high-impact, multi-team programs, resolving dependencies, risks, and delivery bottlenecks.
- Drives adoption of automation, quality gates, and operational readiness criteria for AI and software releases.
- Establishes program-level dashboards to track health, velocity, compliance, and operational outcomes.
Stakeholder & Leadership Engagement
- Acts as the primary point of accountability for AI SDLC maturity across the organization.
- Provides regular, concise updates to senior leadership on progress, risks, trade-offs, and outcomes.
- Builds strong partnerships across development, data science, operations, security, legal, and product teams to drive alignment and shared ownership.
People & Capability Development
- Leads and mentors program managers, technical leads, and DevX contributors aligned to AI SDLC initiatives.
- Guides capability development through role-based enablement, best practices, and change management.
- Influences resourcing and prioritization decisions in collaboration with functional leaders.
Education & Experience Recommended
- Four-year or Graduate Degree in Computer Science, Information Systems, Engineering, or related discipline, or equivalent practical experience.
- Typically 10+ years of experience in software engineering, DevOps, platform engineering, AI/ML systems, or large-scale technical program management.
- 5+ years leading cross-functional technical programs involving multiple engineering and platform teams.
Preferred Certifications
- AWS Certified DevOps Engineer, AWS Machine Learning, Azure AI Engineer, or equivalent cloud/AI certifications.
- Agile / SAFe Program Consultant (SPC), PMP, or similar program leadership certification (preferred).
Knowledge & Skills
Core Technical
- AI/ML Systems & MLOps
- CI/CD for Software and AI Pipelines
- Cloud Platforms (AWS, Azure)
- Automation & Infrastructure as Code
- Observability, Monitoring, and Model Performance Tracking
- Microservices & Distributed Systems
- Security, Compliance, and Access Governance
- APIs and Platform Integration
Software & AI Development
- Python, Java, JavaScript (working knowledge)
- Containers & Orchestration (Docker, Kubernetes)
- Data & Model Lifecycle Management
- Scalability and Reliability Engineering
Program & Leadership
- Agile & Product-Oriented Delivery Models
- Dependency & Risk Management
- Metrics-Driven Execution
- Executive Communication
- Change Management
Cross-Org Skills
- Strategic Thinking
- Customer Centricity
- Prioritization Across Competing Initiatives
- Resilience in Ambiguous Environments
- Influence Without Direct Authority
Impact & Scope
- Impacts large, multi-disciplinary engineering and AI functions across HP.
- Shapes how AI-enabled software is designed, delivered, governed, and operated at scale.
- Directly influences time-to-market, quality, compliance posture, and developer productivity.
Complexity
- Operates in a highly complex, evolving technical and regulatory landscape.
- Applies managerial and program leadership concepts to resolve ambiguous, high-impact problems.
- Achieves outcomes through matrixed teams and indirect leadership.
