Lead AI ML Fin Ops

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 you’re excited by the challenge of combining deep technical expertise with strategic financial planning to create real-world impact, we’d love to have you on board

1. 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. 

2. 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. 

3. 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. 

4. 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. 

5. 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 financ