MLops Engineer

We are looking for a highly skilled MLOps Engineer to build, deploy, automate, and manage Machine Learning solutions in production. The ideal candidate should have hands-on experience in designing scalable ML pipelines, deploying models, automating workflows, monitoring model performance, and implementing CI/CD for machine learning applications. The role requires close collaboration with Data Scientists, Data Engineers, and DevOps teams to ensure reliable and efficient ML model lifecycle management.

Key Responsibilities

• Design, develop, and maintain end-to-end MLOps pipelines for training, testing, deployment, and monitoring of machine learning models.
• Deploy machine learning models to cloud and on-premise environments using containerization and orchestration technologies.
• Build and automate CI/CD pipelines for machine learning applications.
• Develop reusable ML workflows for model training, validation, deployment, and versioning.
• Implement model monitoring, performance tracking, drift detection, and automated retraining strategies.
• Manage ML artifacts, datasets, feature stores, and model registries.
• Collaborate with Data Scientists to operationalize machine learning models.
• Optimize infrastructure for scalable and cost-effective model deployment.
• Troubleshoot production issues and ensure high availability of ML services.
• Maintain security, governance, and compliance standards across ML platforms.

Required Skills

• 5–8 years of IT experience with at least 3+ years of hands-on experience in MLOps.
• Strong programming experience in Python.
• Hands-on experience with one or more MLOps platforms:
• MLflow
• Kubeflow
• Azure Machine Learning
• AWS SageMaker
• Google Vertex AI
• Experience deploying machine learning models using:
• Docker
• Kubernetes
• Strong knowledge of CI/CD tools:
• Jenkins
• Azure DevOps
• GitHub Actions
• GitLab CI/CD
• Experience with version control using Git.
• Strong understanding of machine learning lifecycle and model management.
• Experience with REST APIs for model serving.
• Knowledge of SQL and data engineering concepts.
• Experience with Linux and shell scripting.

Cloud Platforms

Experience with at least one of the following:

• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)

Good to Have

• Experience with Apache Airflow or Prefect for workflow orchestration.
• Knowledge of Terraform or Infrastructure as Code (IaC).
• Experience with Spark or PySpark for large-scale data processing.
• Familiarity with Databricks.
• Experience with Kafka or other streaming platforms.
• Knowledge of Feature Store implementation.
• Experience with monitoring tools such as Prometheus, Grafana, ELK, or Azure Monitor.
• Exposure to LLMOps, Generative AI, or Large Language Models is an added advantage.