Senior Manager - Advanced Technology

Key Responsibilities

• Understand Avangrid Power business, goals, objectives and define AI digital technologies programs for the full value chain.
• Big focus on Advance Operations, Asset & Energy Management digitalization, and support to Growth functions, while ensuring digital solutions modernization, and Optimization.
• Development and implementation of all Power AI digital programs, ensuring they align with organizational goals and regulatory requirements.
• Develops, Maintains and Implements the AI Technology Roadmap.
• Architect and deliver compute stacks for AI/ML (training/inference), physics-based modeling, and streaming analytics, prioritizing reliability, scalability, and total cost of ownership.
• Lead a team of 4 direct reports and 20+ indirect reports delivering an excellent service to the Avangrid Power business.
• Build and lead a high-performing team of platform engineers, ML engineers, prompt/agent engineers, and SREs; provide technical direction, coaching, and career development.
• Responsible to manage the coordination between technology providers and the whole Power business needs, including but not limited to infrastructure provisioning required to build digital solutions, Avangrid business/users technology needs and secure integration of digital within the company technology ecosystem.
• Responsible for the Technologies Lifecycle Management, including the implementation of deployment methodologies required for the different Digital Projects execution.
• Ensure the use of common development standards, methodology and tools.
• Oversee day-to-day technology operations, ensuring high availability, reliability, and performance.
• Deliver excellent levels of AI operational stability and reliability to the digital platforms.
• Establish run-books and observability for platform health; own incident response and continuous improvement; provide operational support to technology operations.
• Foster a culture of continuous transformation.
• Provide leadership and oversight on all aspects of the area, including AI technical engineering, operational readiness, early adopter and pilot programs, technology transition and transformation; communications; technology adoption; service delivery and service support.
• Determine workload placement (on-prem HPC vs. cloud GPU vs. accelerated endpoints), storage tiers, and high-throughput data pipelines serving Trading, Physical Systems, Growth Solutions, and O&M.
• Determine outsourcing vs. in-house build; manage strategic vendors (hardware, cloud, ISVs) to ensure performance, security compliance, and cost discipline.
• Implement rigorous testing and performance benchmarking; enforce standards, peer reviews, change controls; integrate cyber, identity, and data governance across the stack.
• Foster a collaborative and innovative work environment.
• Ensure the enterprise has the capability to support new technology and maintain high levels of service.
• Create a sustainable IT environment to facilitate growth while maintaining required regulatory compliance and end user requirements.

Required Qualifications

• 10+ Years of experience in any capacity working with Technology and digital tools.
• 10+ years in advanced computing, HPC/GPU platforms, or large-scale data/AI systems; 3–5+ years building and operating production ML/analytics platforms in mission-critical environments.
• Proven experience translating business goals into compute architectures and products, delivering tangible outcomes in energy, industrials, or similarly complex operations.
• Hands-on fluency with GPU/HPC orchestration, workload schedulers, and containerized stacks (e.g., Kubernetes), plus observability/performance benchmarking.
• Hands-on experience into AI platforms available in public cloud providers (Azure, AWS).
• Demonstrated leadership of cross-functional teams and multi-vendor ecosystems with strong stakeholder influence.
• Knowledge of IT control policies, security standards, and data governance; ability to embed these into platform design and operations.

Preferred Qualifications

• Experience integrating compute platforms with forecasting, condition monitoring, or real-time OT/IT data (PI/data historians, SCADA) and trading applications.
• Background in Renewables or grid-scale operations; familiarity with model lifecycle (feature engineering, retraining, drift management).
• Master’s degree in a relevant field (e.g., Computer Engineering, Data Science) or equivalent experience.