Machine Learning Operations (MLOps) Engineer

Here’s a clean, comprehensive, LinkedIn-ready Job Description for a 2+ years MLOps role, written the way recruiters and serious candidates expect it. You can paste this directly into LinkedIn Jobs with minimal edits.

Machine Learning Operations (MLOps) Engineer – 2+ Years Experience

Hyderabad (Onsite for first 2 months), then Remote

Night Shift: 9:00 PM – 5:00 AM IST

Full-time

We are hiring an MLOps Engineer for one of our enterprise clients. This role sits at the intersection of machine learning, software engineering, cloud infrastructure, and DevOps. You will be responsible for building, deploying, monitoring, and scaling machine learning systems in production environments.

The ideal candidate has hands-on experience working with real-world ML pipelines, understands production constraints, and can collaborate effectively with data scientists, backend engineers, and platform teams.

• Design, build, and maintain end-to-end ML pipelines from data ingestion to model deployment.
• Productionize machine learning models with a focus on scalability, reliability, and performance.
• Automate model training, validation, versioning, and deployment workflows.
• Implement CI/CD pipelines for ML systems (ML pipelines, not just application code).
• Monitor deployed models for performance drift, data drift, and model degradation.
• Manage infrastructure for ML workloads across cloud and containerized environments.
• Ensure reproducibility, traceability, and governance of ML experiments and models.
• Collaborate closely with data scientists to convert research models into production-grade services.
• Troubleshoot production issues related to data, models, pipelines, and infrastructure.
  

• Minimum 2 years of hands-on experience in MLOps, ML Engineering, Data Engineering, or DevOps with ML systems.
• Experience supporting production machine learning systems, not just experimentation or notebooks.
  

• Strong proficiency in Python (required).
• Solid understanding of software engineering best practices (modular code, testing, version control).
• Experience working with REST APIs and microservices.
• Familiarity with Git-based workflows (GitHub / GitLab / Bitbucket).
  

• Understanding of ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.
• Familiarity with common ML algorithms and model types (classification, regression, NLP, time series, etc.).
• Experience with ML frameworks such as:
• TensorFlow / PyTorch / Scikit-learn (at least one)
• Understanding of feature engineering, model evaluation metrics, and bias/variance tradeoffs.
  

• Hands-on experience with MLOps tools such as:
• MLflow (tracking, model registry)
• Kubeflow / SageMaker / Vertex AI (any one is sufficient)
• Experience with model versioning and experiment tracking.
• Knowledge of model serving patterns (batch, real-time, streaming).
  

• Experience building and maintaining data pipelines.
• Working knowledge of:
• SQL
• Data processing frameworks (Spark, Airflow, or similar)
• Understanding of data validation, schema evolution, and data quality checks.
  

• Experience with at least one cloud provider:
• AWS / GCP / Azure
• Hands-on experience with:
• Docker
• Kubernetes (basic to intermediate level)
• Familiarity with infrastructure concepts like:
• Compute, storage, networking
• IAM and security basics
  

• Experience building CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, etc.).
• Understanding of DevOps practices applied to ML workflows.
• Familiarity with artifact repositories and model registries.
  

• Experience monitoring:
• Model performance
• Data drift
• System health
• Familiarity with logging and monitoring tools such as:
• Prometheus, Grafana, CloudWatch, Datadog, or similar
• Ability to debug issues across data, code, and infrastructure layers.
  

• Experience with feature stores (Feast or similar).
• Knowledge of data privacy, security, and compliance in ML systems.
• Exposure to NLP, recommendation systems, or large-scale ML systems.
• Experience working in night shift or distributed global teams.
• Understanding of cost optimization for ML workloads in the cloud.
  

• First 2 months: Onsite in Hyderabad (mandatory).
• Post 2 months: Fully Remote.
• Shift Timing: 9:00 PM – 5:00 AM IST (mandatory).
  

This role is ideal for candidates who:

• Have moved beyond notebooks and experiments.
• Enjoy solving real-world production problems in ML systems.
• Are comfortable working across ML, software, and infrastructure boundaries.
• Want to grow into a strong, well-rounded MLOps professional.