Sr. Manager, AI Strategy, FinOps & Optimization- FreeWheel

Comcast•New York, NY

6d•$178,973 - $268,459

About The Position

Responsible for establishing a culture of engineering performance, scalability, and financial optimization through the strategic adoption of AI, machine learning, and data platform technologies. Partners with Engineering, Product, Architecture, Data, and Finance teams to evaluate and implement scalable, cost-effective AI solutions across enterprise technology investments. Drives optimization initiatives leveraging platforms such as Snowflake, Aerospike, and Databricks while applying FinOps principles, financial modeling, and operational analytics to improve efficiency, reduce infrastructure costs, and maximize business value. Focuses on identifying, evaluating, and operationalizing AI capabilities, commercial software solutions, and emerging technologies rather than building foundational AI/ML models.

Requirements

Experience delivering AI, machine learning, or LLM-enabled business solutions in enterprise environments.
Strong understanding of AI/ML concepts and practical application of large language models, including evaluation, adoption, and operationalization.
Experience with FinOps practices, cloud cost management, financial planning, budgeting, and financial modeling.
Deep experience with Snowflake and its AI/ML ecosystem, optimization capabilities, and cost management best practices.
Experience with Aerospike and large-scale distributed data platforms.
Experience with Databricks for analytics, data engineering, and AI-related use cases.
Familiarity with BI and analytics tools such as Looker.
Experience evaluating and implementing commercial off-the-shelf (COTS) software products and technology platforms.
Strong ability to translate technical platform capabilities into measurable business and financial outcomes.
Experience partnering with Engineering, Architecture, Finance, and Product teams to drive enterprise-scale optimization initiatives.
Bachelor's Degree
7-10 Years Relevant Work Experience

Responsibilities

Lead the identification, prioritization, and execution of cost optimization initiatives across audience, identity, measurement, decisioning, activation, and platform engineering domains.
Develop and implement AI-driven solutions that improve engineering productivity, automate repetitive tasks, reduce operational overhead, and accelerate software delivery.
Establish and monitor engineering efficiency metrics, including infrastructure utilization, cloud spend, cost per transaction, cost per audience activation, developer productivity, and platform unit economics.
Lead cloud and infrastructure cost management initiatives across public cloud, data platforms, and distributed systems environments, ensuring optimal resource utilization and financial accountability.
Create governance processes that incorporate cost, efficiency, and operational sustainability into architectural reviews, technical design decisions, and investment planning.
Leverage artificial intelligence and advanced analytics to identify cost anomalies, forecast capacity needs, recommend optimization opportunities, and improve financial predictability.
Develop executive dashboards and reporting mechanisms that provide visibility into engineering spend, infrastructure utilization, AI adoption, productivity gains, and realized business value.
Drive continuous improvement initiatives focused on reducing technical debt, eliminating redundant systems, simplifying platform operations, and improving engineering velocity.
Build organizational awareness and accountability for engineering efficiency through education, coaching, performance metrics, and operational best practices.
Establish forecasting and planning processes that improve budget accuracy, resource allocation, and long-term infrastructure planning
Define and execute strategies for incorporating AI capabilities into enterprise data platforms, with particular focus on Snowflake, Aerospike, and Databricks environments.
Evaluate commercial off-the-shelf (COTS) technologies, AI-enabled products, and platform capabilities to accelerate adoption and reduce implementation complexity.
Assess emerging AI, machine learning, and large language model (LLM) technologies and determine practical use cases that drive productivity, automation, and cost efficiency.
Collaborate with platform engineering teams to optimize workload placement, platform architecture, and storage/compute consumption across Snowflake, Aerospike, and Databricks.
Establish measurement frameworks and success metrics to quantify the business impact of AI-driven optimization initiatives.