ML Ops Lead

Job Description:

Mandatory Skills:

• Machine Learning (ML)
• CI/CD (for ML pipelines)
• Model Deployment & Serving
• Model Lifecycle Management
• Model Registry & Experiment Tracking
• Python

Additional Skills:

• Advanced GenAI & Agentic Framework Concepts
• NLP (Natural Language Processing)
• PyTorch
• Databricks

Key Responsibilities:

• Define and drive enterprise MLOps strategy aligned with organizational AI, machine learning, and operational objectives.
• Establish MLOps governance frameworks, engineering standards, and deployment best practices across teams.
• Lead the design of enterprise-scale CI/CD architectures supporting machine learning lifecycle automation.
• Define enterprise standards for model deployment, serving, and operational management.
• Establish governance standards for model registry, version management, model traceability, and lifecycle control.
• Define experiment tracking standards supporting reproducibility, auditability, and model governance requirements.
• Drive automation initiatives focused on accelerating model deployment, operational stability, and release consistency.
• Define enterprise standards for model monitoring, prediction quality evaluation, data drift detection, concept drift analysis, model health monitoring, and retraining governance to ensure long-term production model effectiveness.
• Lead architecture reviews and ensure MLOps solutions meet scalability, reliability, maintainability, and governance objectives.
• Guide teams on MLOps architecture, deployment frameworks, model lifecycle management, and operational best practices.
• Identify deployment risks, governance gaps, operational bottlenecks, and lifecycle management challenges while defining mitigation strategies.
• Collaborate with various teams and leadership stakeholders to align MLOps initiatives with organizational objectives.
• Drive continuous improvement initiatives focused on MLOps maturity, automation effectiveness, operational excellence, and governance.

Behavioral Competencies:

• Demonstrates leadership and accountability in driving MLOps excellence across programs and initiatives.
• Collaborates effectively with various teams and business stakeholders to ensure smooth delivery.
• Promotes a culture of automation, reliability, governance, and continuous improvement.
• Applies strategic thinking to address deployment challenges, operational risks, and business priorities.
• Demonstrates strong decision-making while balancing scalability, reliability, governance, and delivery objectives.
• Communicates effectively regarding MLOps strategy, risks, dependencies, and operational outcomes.
• Maintains a proactive approach toward engineering governance, operational maturity, and solution excellence.
• Encourages innovation and continuous learning within MLOps, machine learning operations, and deployment engineering practices.
• Drives collaboration and alignment across teams to achieve organizational objectives.
• Demonstrates a customer-centric mindset by delivering reliable, scalable, and high-quality ML operational solutions.

Skills

CI / CD, Cicd, MLOps, Python