MLOps Engineer

Location: Electronic City Phase I

Job Description

- At Aarvian, while technology drives our business, it is our global and diverse culture that underpins our success. We value our people and are committed to fostering an environment grounded in transparency, diversity, integrity, continuous learning, and growth. If working in an environment that encourages you to innovate and excel—not just professionally, but personally—interests you, you’ll enjoy your career with us!

- Location:

Remote

Experience:

3–6 years
- Notice Period:
Immediate to 30 Days (Max)

- Role Description
- We are hiring for the role of MLOps Engineer. You will be responsible for building, automating, deploying, and monitoring machine learning solutions  will work closely with Data Scientists, ML Engineers, and Software Development teams to operationalize machine learning models and ensure reliable, secure, and scalable AI systems across cloud environments.

- Key Responsibilities
• Design, build, and maintain end-to-end ML pipelines for training, deployment, monitoring, and retraining of machine learning models.

• Implement CI/CD and CT (Continuous Training) pipelines for ML applications using modern Dev Ops practices.

• Deploy, manage, and optimize machine learning workloads on Azure ML and cloud infrastructure.

• Containerize ML applications using Docker and orchestrate deployments using Kubernetes.

• Manage model lifecycle, versioning, experiment tracking, and model registry using MLflow or similar tools.

• Develop automated workflows using Airflow, Azure Data Factory, or equivalent orchestration platforms.
• Monitor model performance, drift, latency, and infrastructure health using monitoring and alerting tools.
• Collaborate with Data Scientists to product ionize machine learning models and improve deployment efficiency.

• Ensure security, scalability, reliability, and governance standards across ML systems.

• Troubleshoot production issues and optimize infrastructure for performance and cost efficiency.

• Work in an Agile environment while maintaining high-quality delivery standards.

Skills & Qualifications

•

Education:

UG – Any Graduate | PG – Any Postgraduate

• 3–6 years of experience in MLOps, Machine Learning Engineering, or Cloud Engineering.

• Strong proficiency in Python, SQL, Linux, and Git.

• Hands-on experience with Azure ML Services, Azure Dev Ops, or equivalent cloud platforms.

• Experience with Docker, Kubernetes, and containerized deployments.

• Knowledge of CI/CD tools such as Azure Dev Ops, Git Hub Actions, Jenkins, or Git Lab CI/CD.

• Experience with ML lifecycle management tools such as MLflow, Kubeflow, or similar platforms.

• Familiarity with workflow orchestration tools such as Airflow, Prefect, or Azure Data Factory.

• Understanding of monitoring and observability tools such as Prometheus, Grafana, or Azure Monitor.

• Strong problem-solving, communication, and stakeholder management skills.

• Experience with Databricks, Terraform, or Infrastructure as Code (IaC) tools will be an added advantage.