ML Operations Manager

Our site in Pune, India is seeking a Manager, ML Operations to join our IT Digital Platform team and lead the operationalization of machine learning and AI solutions at enterprise scale. In this role, you will be responsible for building, scaling, and governing MLOps capabilities that enable reliable, secure, and compliant deployment of ML systems across the company.
You will manage a team of MLOps and ML platform engineers while working closely with AI Engineers, Data Platform teams, Cloud & Security, and business stakeholders. This role is ideal for a technically strong leader who can balance hands-on architecture with team leadership, platform strategy, and measurable business impact.

What You’ll Do:

MLOps Leadership & Architecture

Design and build enterprise MLOps architecture supporting classical ML workloads across multiple business domains.
Define and enforce standards for the full model lifecycle: training, evaluation, deployment, monitoring, and retirement.
Establish scalable patterns for CI/CT/CD, model versioning, feature management, and experiment tracking so ML engineers can develop and iterate independently.
Build the platform with multi-domain use cases in mind, the same infrastructure must serve models across operations, supply chain, quality, and business functions.
Partner with AI Engineering leadership to align platform capabilities with business priorities.

Platform & Operations

Own the reliability, performance, and cost efficiency of production ML systems.
Build shared MLOps services and reusable components on Google Cloud Platform.
Implement model observability: feature distribution drift, prediction variance, cost per model, and latency.
Establish incident response and root cause analysis practices for ML systems.

Data Engineering and Feature Pipelines

Own data pipeline design for ML workloads: structured and unstructured data, batch and streaming sources, varying data freshness and quality characteristics across business domains.
Build feature engineering pipelines with robust data quality gates, automated validation, and support for diverse data sources.
Partner with the Data Platform team on data ingestion, quality monitoring, and integration with the enterprise data layer.
Work directly with domain experts like process engineers, quality teams, supply chain analysts, and business analysts to translate domain knowledge into features and model requirements.

Team Leadership & Enablement

Lead, mentor, and grow a team of MLOps and platform engineers.
Set clear goals, technical direction, and delivery expectations aligned to business outcomes.
Foster a culture of engineering excellence, automation, and operational rigor.
Partner with Product Owners and AI Engineers to accelerate delivery through self-service tooling and enablement.

Governance, Security & Responsible AI

Operationalize responsible AI principles across the ML lifecycle, including security, privacy, compliance, and auditability.
Work closely with Security, Risk, and Legal teams to implement guardrails, access controls, and policy enforcement.
Ensure AI systems meet enterprise governance requirements and are production-ready by design.

Delivery & Business Impact

Translate business and product requirements into scalable MLOps capabilities.
Measure and report on platform adoption, operational health, and business impact.
Drive continuous improvement to reduce time-to-production, increase reliability, and lower operational cost.

What We Seek:

Required Skills

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
7+ years of experience in ML engineering, platform engineering, or MLOps, including people leadership responsibilities.
Strong experience building and operating ML platforms on Google Cloud Platform (GCP).
Deep understanding of CI/CD, infrastructure automation, and cloud-native architectures.
Proficiency in Python and experience with ML frameworks and model serving patterns.
Experience operating production systems with high availability, observability, and compliance requirements.
Strong communication skills and ability to work across technical and business teams.

Preferred Skills

Experience with Vertex AI (training, pipelines, model registry, deployment, monitoring).
Hands-on experience supporting LLMs and agentic AI systems in production.
Familiarity with feature stores, experiment tracking, and model governance tools.
Infrastructure as Code experience (e.g., Terraform).
Knowledge of data platforms and analytics tooling (e.g., BigQuery).
Experience defining enterprise standards and reference architectures for AI platforms

What We Offer:

At Entegris, we invest in providing opportunity to our employees and promote from within. The new hire in this role will have the potential to grow and create relationships across the organization and be recognized for demonstrated success and adherence to company PACE values.

Our total rewards package goes above and beyond just a paycheck. Whether you’re looking to build your career, improve your health, or protect your wealth, we offer generous benefits to help you achieve your goals.

Excellent health, dental and vision insurance packages to fit your needs
Flexible work schedule
Paid time off (PTO) policy that empowers you to take the time you need to recharge
Education assistance to support your learning journey
Values-driven culture with colleagues that rally around People, Accountability, Creativity and Excellence.

At Entegris we are committed to providing equal opportunity to all employees and applicants. Our policy is to recruit, hire, train, and reward employees for their individual abilities, achievements and experience without regard to race, color, religion, sexual orientation, age, national origin, disability, marital or military status.

GoAsia Talent is proud to partner with Entegris in building their growing team in Pune, India. We invite you to explore all the available opportunities at Entegris and take the next step in your career.