ML OPS - Lead

Why Join Iris? Are you ready to do the best work of your career at one of India’s Top 25 Best Workplaces in IT industry ? Do you want to grow in an award-winning culture that truly values your talent and ambitions ? Join Iris Software — one of the fastest-growing IT services companies — where you own and shape your success story .

About Us At Iris Software, our vision is to be our client’s most trusted technology partner, and the first choice for the industry’s top professionals to realize their full potential.
With over 4,300 associates across India, U.S.A, and Canada, we help our enterprise clients thrive with technology-enabled transformation across financial services, healthcare, transportation & logistics, and professional services.
Our work covers complex, mission-critical applications with the latest technologies, such as high-value complex Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

Working with Us At Iris, every role is more than a job — it’s a launchpad for growth.
Our Employee Value Proposition, “Build Your Future. Own Your Journey.” reflects our belief that people thrive when they have ownership of their career and the right opportunities to shape it.
We foster a culture where your potential is valued, your voice matters, and your work creates real impact. With cutting-edge projects, personalized career development, continuous learning and mentorship, we support you to grow and become your best — both personally and professionally.
Curious what it’s like to work at Iris? Head to this video for an inside look at the people, the passion, and the possibilities. Watch it here (https://www.youtube.com/watch?v=QLUs9V5xw0k) .
## Job Description

Experience: 8-10 Years

Location: Gurugram

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.

• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.

• Promotes a culture of automation, reliability, governance, and continuous improvement.

• Apply 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.

Perks and Benefits for Irisians Iris provides world-class benefits for a personalized employee experience. These benefits are designed to support financial, health and well-being needs of Irisians for a holistic professional and personal growth. Click here to view the benefits.