Lead - AI/ML FinOps Solutions

Description : As the Lead AI/ML FinOps for one of the largest International Media & Entertainment companies, you will drive the financial optimization of AI/ML operations at scale, ensuring our systems are both cutting-edge and cost-efficient.

With AI/ML technologies playing a critical role in content creation, personalization, and audience engagement, your expertise will be instrumental in optimizing resource utilization, managing budgets, and implementing innovative cost-saving strategies.

Based in India, you will lead the charge in setting up and evolving a robust AI/ML financial operations framework, collaborating closely with engineering, data science, and finance teams.

This role is perfect for someone passionate about AI/ML technology and operational excellence, with a strong focus on financial stewardship.

If youre excited by the challenge of combining deep technical expertise with strategic financial planning to create real-world impact, wed love to have you on board

AI/ML Financial Planning and Cost Optimization :

- 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.

Cloud and Infrastructure Cost 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.

Collaboration and Stakeholder Engagement :

- 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.

AI/ML Workflow and Lifecycle Management :

- 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.

Innovation and Continuous Improvement :

- 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.

Qualifications & Experiences :

Academic Qualifications :

- Bachelors or Masters 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.

Professional Experience :

- 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.

Technical Skills :

- 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.

Other Skills :

- 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