Job Description

Experience: 4 to 10 years

Location: Work from Office

Notice Period: Immediate joiners or a maximum of 15 days

About Us

NStarX is an AI-native, cloud-native services & platform company that helps enterprises accelerate digital transformation through:

• AI Engineering & Data Modernization
• Enterprise GenAI & Agentic AI
• Federated Learning & Confidential Computing
• Predictive & Prescriptive AI Solutions
• “Service-as-Software” – our core platform model
• Domain solutions across Healthcare, Media, Manufacturing, Finance & Retail

For more information, please visit:
https://nstarxinc.com/

Role Summary

As an MLOps Engineer, you will be responsible for building, maintaining, and optimizing machine learning (ML) operations
infrastructure to enable smooth deployment and scaling of ML models.

You will collaborate with data scientists, software engineers, and IT teams to streamline model workflows, enhance
automation, and ensure high availability of ML solutions.

Key Responsibilities

• Develop and manage infrastructure for end-to-end ML workflows including model training, deployment, monitoring, and maintenance.
• Implement CI/CD pipelines for ML models and data workflows.
• Collaborate with cross-functional teams to build scalable and robust ML infrastructure on cloud and on-premises environments.
• Monitor and optimize model performance and infrastructure to ensure efficient resource usage.
• Manage data versioning and model versioning across multiple environments.
• Implement security, governance, and compliance protocols in ML deployment and data pipelines.
• Support troubleshooting, debugging, and incident management for ML infrastructure issues.

Qualifications

Experience

• 4 to 10 years of experience in MLOps, DevOps, or ML Engineering.

Technical Skills

• Strong proficiency with cloud platforms such as AWS, Azure, or GCP.
• Experience with containerization and orchestration tools like Docker and Kubernetes.
• Hands-on experience with ML model deployment, monitoring, and scaling.
• Proficiency with CI/CD tools such as Jenkins or GitLab CI.
• Familiarity with data versioning and management tools such as DVC.
• Strong coding skills in Python with knowledge of ML libraries like TensorFlow or PyTorch.

Soft Skills

• Strong problem-solving skills and ability to work in a collaborative environment.
• Effective communication skills for cross-functional teamwork.

Preferred Qualifications

• Experience with MLOps frameworks such as MLflow or Kubeflow.
• Knowledge of data engineering best practices and data pipeline tools like Apache Airflow or Kafka.
• Certification in any cloud platform such as AWS, Azure, or GCP.

Benefits

• Competitive compensation.
• Opportunity to work with a dynamic team on cutting-edge AI and ML solutions.
• Professional growth and development opportunities.