Machine Learning Engineer

Role -ML OPS

Here is a Job opportunity for you in IRIS-

At Iris Software, our vision is to be our clients 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 at Iris: Be valued, be inspired, be your best.

At Iris Software, we invest in and create a culture where colleagues feel valued, can explore their potential, and have opportunities to grow.

Our employee value proposition is about Being Your Best as a professional and person. It is about being challenged by work that inspires us, being empowered to excel and grow in your career, and being part of a culture where talent is valued. Were a place where everyone can belong.

About the Role: Senior Engineer (ML- Ops)

Location: Hybrid (Noida, Delhi, Pune)

Role Overview: We are seeking a Senior ML Ops Engineer to Lead to optimize our machine learning deployment and operations ecosystem. The role involves designing scalable ML infrastructure, automating pipelines, ensuring reliable model performance, and collaborating with cross-functional teams to deliver production-grade AI solutions.

Position: ML Operations

Location: Noida / Pune / Gurugram

Experience: 6-11 years

Job Type: Full-time

Key Responsibilities:

6+ years of experience in ML Ops engineering or a related field.

• Architect and manage scalable ML infrastructure for model training, deployment, and monitoring.

• Design and automate end-to-end ML pipelines (data ingestion training validation deployment monitoring).

• Implement CI/CD and CT (Continuous Training) frameworks for ML lifecycle automation.

• Ensure reproducibility, versioning, and traceability of models and datasets (using tools like MLflow, DVC, or Kubeflow).

• Integrate monitoring for model drift, data quality, and model performance in production.

• Collaborate with data scientists, data engineers, and DevOps teams to standardize ML workflows.

• Optimize cost and scalability across multi-cloud or hybrid environments (AWS, GCP, Azure).

• Maintain and document best practices for ML system governance, auditability, and compliance.

• Mentor junior engineers and contribute to technical roadmap discussions.

Key Responsibilities:

• Architect and manage scalable ML infrastructure for model training, deployment, and monitoring.

• Design and automate end-to-end ML pipelines (data ingestion training validation deployment monitoring).

• Implement CI/CD and CT (Continuous Training) frameworks for ML lifecycle automation.

• Ensure reproducibility, versioning, and traceability of models and datasets (using tools like MLflow, DVC, or Kubeflow).

• Integrate monitoring for model drift, data quality, and model performance in production.

• Collaborate with data scientists, data engineers, and DevOps teams to standardize ML workflows.

• Optimize cost and scalability across multi-cloud or hybrid environments (AWS, GCP, Azure).

• Maintain and document best practices for ML system governance, auditability, and compliance.

• Mentor junior engineers and contribute to technical roadmap discussions.

Add On-

• Strong proficiency in Python, with experience in machine learning libraries such as TensorFlow, PyTorch, scikit-learn, etc.
• Extensive experience with ML Ops frameworks like Kubeflow, MLflow, TensorFlow Extended (TFX), KubeFlow Pipelines, or similar.
• Strong experience in deploying and managing machine learning models in cloud environments (AWS, GCP, Azure).
• Proficiency in managing CI/CD pipelines for ML workflows using tools such as Jenkins, GitLab, CircleCI, etc.
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) technologies for model deployment.
• Skills : SageMaker, PySpark, AWS Services.

If interested, Kindly share your resume on

Notice period- Immediate to 1 month max