Lead AI ML Fin Ops

Develop and implement strategies to optimize the cost of AI/ML workloads across cloud and on-premise environments.

Analyze and forecast AI/ML resource consumption, creating actionable insights to drive financial efficiency.

Establish KPIs and benchmarks for AI/ML operational costs, ensuring alignment with budgetary goals.

Collaborate with cloud vendors to negotiate contracts and optimize pricing models for AI/ML services.

Identify opportunities for cost reduction through model performance improvements, workload optimizations, and better infrastructure management.

Lead initiatives to optimize cloud resource allocation for AI/ML workloads, ensuring scalability and efficiency.

Monitor and manage the financial impact of AI/ML resource utilization, leveraging tools like FinOps platforms and custom analytics dashboards.

Drive adoption of cost-aware practices across teams, promoting efficient use of compute, storage, and networking resources.

Establish governance policies to prevent cost overruns and ensure compliance with organizational financial guidelines.

Recommend strategies for multi-cloud and hybrid-cloud deployments to achieve cost and performance objectives.

Partner with data science, engineering, and product teams to align AI/ML operational goals with business priorities.

Work closely with finance and procurement teams to streamline budgeting, reporting, and approval processes for AI/ML initiatives.

Act as a bridge between technical teams and business leaders, ensuring clear communication of financial trade-offs and benefits.

Build strong relationships with cloud service providers to leverage their expertise and tools for cost optimization.

Advocate for financial best practices within AI/ML operations, fostering a culture of accountability and cost-conscious innovation.

Oversee the end-to-end lifecycle of AI/ML models, focusing on efficient resource usage during development, training, and deployment phases.

Implement cost-aware model retraining schedules based on business needs and performance thresholds.

Collaborate with MLOps teams to streamline model deployment pipelines, reducing operational overheads.

Monitor and analyze the financial impact of different AI/ML use cases to prioritize high-value projects.

Develop processes to decommission obsolete models and infrastructure to avoid unnecessary expenditures.

Stay updated on emerging technologies and trends in AI/ML FinOps, leveraging innovations to enhance cost efficiency.

Lead the evaluation and adoption of tools and frameworks for AI/ML cost monitoring and optimization.

Promote a mindset of continuous improvement, encouraging teams to experiment with cost-effective methodologies.

Drive internal knowledge-sharing sessions to disseminate best practices in AI/ML financial management.

Collaborate on global initiatives to standardize and improve AI/ML FinOps practices across the organization.

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Finance, or a related field.

Specialized certifications in FinOps, Cloud Cost Management, or AI/ML technologies are highly desirable.

6+ years  of experience in AI/ML operations, cloud cost optimization, or a related field, with a strong emphasis on financial planning and governance.

Proven track record of managing AI/ML workloads in cloud environments such as AWS, Azure, or Google Cloud.

Extensive experience in building and implementing FinOps strategies for large-scale AI/ML projects.

Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and their computational requirements.

Expertise in budgeting, cost forecasting, and financial modeling for technology initiatives.

Proficiency in cloud cost management tools like AWS Cost Explorer, Azure Cost Management, or third-party solutions (e.g., CloudHealth, Apptio).

Strong understanding of AI/ML infrastructure, including GPUs, TPUs, and distributed computing architectures.

Experience in automating financial reporting and monitoring using analytics tools and scripts (e.g., Python, SQL).

Knowledge of MLOps practices and tools, including CI/CD pipelines, model monitoring, and version control.

Strong data analysis skills to identify cost-saving opportunities and drive actionable insights.

Excellent leadership and communication skills, with the ability to influence stakeholders across levels.

Strong problem-solving and analytical abilities, with a focus on financial impact and operational efficiency.

Proactive mindset, with a passion for innovation and continuous improvement.

Proven ability to work collaboratively in a cross-functional and dynamic environment.

Strong organizational skills, with the ability to manage multiple priorities and deliver results under tight deadlines.