ML OPS - Senior Engineer

Iris Software, Inc.; India; On-site; INR 1,500,000 - 2,600,000; Full time

Job summary

Iris Software is a leading IT services company in India known for its rapid growth and award-winning culture. The role focuses on building and scaling MLOps capabilities across ML lifecycles, with emphasis on deployment automation, monitoring, and governance.

The candidate will work with cross-functional teams to deliver end-to-end ML platform capabilities, including model deployment, registry, experiment tracking, and observability, while promoting automation and reliability.

Qualifications

• Experience in ML Ops, CI/CD for ML pipelines, and deployment automation.

Responsibilities

• Design scalable CI/CD frameworks for ML lifecycle management.
• Define deployment architectures and serving strategies.
• Lead automated ML pipeline implementations for validation, testing, release, and deployment.

Skills

ML Ops

Tools

CI/CD tools

Job description

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.

Job Description

Mandatory Skills

Machine Learning (ML), CI/CD (for ML pipelines), Data Pipeline & Feature Management, Model Deployment & Serving, Model Lifecycle Management, Model Registry & Experiment Tracking, Monitoring & Observation, Python

Key Responsibilities

• Design and implement scalable CI/CD frameworks for machine learning lifecycle management and deployment automation.
• Define model deployment architectures and serving strategies aligned with business and operational requirements.
• Lead implementation of automated ML pipeline solutions supporting model validation, testing, release, and deployment processes.
• Design and optimize model serving frameworks to improve scalability, reliability, and operational efficiency.
• Establish model registry standards for model versioning, governance, traceability, and lifecycle management.
• Define experiment tracking frameworks to support reproducibility, auditability, and model performance management.
• Design and implement model monitoring frameworks to evaluate prediction quality, model performance, data drift, concept drift, and operational health while supporting proactive model lifecycle management and retraining strategies.
• Establish deployment validation and model quality assurance practices to improve production readiness.
• Review ML pipeline designs and deployment implementations to ensure adherence to engineering and operational standards.
• Troubleshoot complex deployment, serving, and ML lifecycle management challenges through detailed root cause analysis.
• Mentor team members on MLOps practices, deployment automation, model lifecycle management, and operational excellence.
• Collaborate with various teams and stakeholders to support end‑to‑end ML platform delivery.
• Drive continuous improvement initiatives focused on automation, reliability, governance, and operational efficiency.
• Demonstrates strong ownership while driving MLOps excellence and operational effectiveness.
• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.
• Promotes automation‑first engineering through proactive optimization and continuous improvement.
• Applies strong analytical thinking to evaluate complex ML deployment and lifecycle management challenges.
• Demonstrates adaptability while managing evolving MLOps technologies and business requirements.
• Communicates effectively regarding deployment status, risks, dependencies, and improvement opportunities.
• Maintains high attention to detail across pipeline design, deployment automation, validation, and operational processes.
• Encourages continuous improvement in MLOps practices, deployment frameworks, and lifecycle management processes.
• Supports knowledge sharing and mentoring to strengthen team capabilities.
• Balances scalability, reliability, governance, and business priorities while driving delivery excellence.

Data & AI - MLOPS - CI/CD (for ML pipelines)

Data & AI - MLOPS - Model Deployment & Serving

Data & AI - MLOPS - Model Lifecycle Management

Data & AI - MLOPS - Monitoring & Observation

Data & AI - MLOPS - Python

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.