MLOps Engineer

MLOps Engineer Python | ML Deployment | Docker | Kubernetes | Cloud

Company: Spark Techwave InnovationZ Pvt Ltd
Job Type: Full-Time
Experience: 26 Years
Location: Chennai / Remote / Hybrid
Department: AI / ML Engineering / DevOps

Job Summary

We are looking for an MLOps Engineer to build and maintain the infrastructure, automation, and deployment processes required to take machine learning models from development to production.

The ideal candidate should have experience with Python, machine learning workflows, CI/CD, Docker, Kubernetes, cloud platforms, model deployment, and monitoring.

The candidate will work closely with Data Scientists, ML Engineers, Data Engineers, and DevOps teams to build reliable, scalable, and production-ready ML systems.

Key Responsibilities

• Design and implement MLOps pipelines for machine learning model development and deployment.
• Automate ML model training, validation, testing, and deployment workflows.
• Build and maintain CI/CD pipelines for ML applications.
• Containerize ML applications using Docker.
• Deploy and manage ML workloads using Kubernetes.
• Implement model versioning, experiment tracking, and artifact management.
• Deploy machine learning models to development, staging, and production environments.
• Monitor model performance, application health, latency, and resource utilization.
• Implement model/data monitoring and alerting mechanisms.
• Manage ML infrastructure across cloud environments.
• Work with Data Scientists and ML Engineers to productionize models.
• Automate infrastructure and deployment processes.
• Troubleshoot production ML systems and resolve deployment issues.
• Implement security, scalability, reliability, and cost optimization practices.
• Maintain technical documentation for ML pipelines and infrastructure.

Required Skills

• Strong experience with Python.

• Understanding of Machine Learning lifecycle and model deployment.

• Experience with Docker and containerization.

• Experience with Kubernetes.

• Hands-on experience with CI/CD pipelines.

• Knowledge of Git and version control.

• Experience with at least one cloud platform:

• AWS

• Azure

• GCP

• Knowledge of Linux/Unix environments.

• Understanding of REST APIs and microservices.

• Knowledge of ML model serving and deployment.

• Understanding of monitoring and logging.

MLOps Tools

Experience with one or more of the following:

• MLflow
• Kubeflow
• Airflow
• DVC
• GitHub Actions
• GitLab CI/CD
• Jenkins
• Argo CD
• Prometheus
• Grafana
• Docker
• Kubernetes

Good to Have

• Experience with AWS SageMaker, Azure ML, or Google Vertex AI.
• Experience with Terraform or Infrastructure as Code.
• Knowledge of Spark / Databricks.
• Experience with model monitoring and drift detection.
• Knowledge of Feature Stores.
• Experience with GPU-based ML workloads.
• Knowledge of Deep Learning deployment.
• Experience deploying GenAI / LLM applications.
• Knowledge of LLMOps.
• Experience with vector databases and RAG applications.
• Knowledge of security and cloud cost optimization.

Education

Bachelor's or Master's degree in:

• Computer Science
• Information Technology
• Artificial Intelligence
• Data Science
• Engineering
• Or a related technical field.

Preferred Candidate Profile

• 26 years of experience in MLOps, ML Engineering, DevOps, or a related role.
• Strong understanding of the end-to-end machine learning lifecycle.
• Experience taking ML models from development into production.
• Strong automation and troubleshooting skills.
• Good understanding of cloud infrastructure and DevOps practices.
• Ability to collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams.

Send your resume at [email protected]

Key Skills

MLOps, MLOps Engineer, Machine Learning, ML Deployment, Python, Docker, Kubernetes, CI/CD, Jenkins, GitHub Actions, GitLab CI/CD, MLflow, Kubeflow, Airflow, AWS, Azure, GCP, AWS SageMaker, Azure ML, Vertex AI, Model Deployment, Model Monitoring, Model Serving, DevOps, Linux, Terraform, Git, Prometheus, Grafana, DVC, Machine Learning Lifecycle, LLMOps