Devops MLops Engineer

Job Description

5+ years of good experience in MLOps.
A talented MLOps Engineer help operationalize machine learning models  ideal candidate will have a strong background in machine learning, software engineering, and Dev Ops practices, with expertise in deploying, monitoring, and maintaining ML models in production environments.
Strong experience in MLOps, Dev Ops, or related fields.
Proficiency in Python and experience with ML frameworks such as Tensor Flow, PyTorch, or Scikit-learn.

Hands-on experience with cloud platforms (e.g., AWS, GCP, or Azure) and their ML services.
Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).

Experience with CI/CD tools (e.g.Git Hub Actions or Jenkins).
Familiarity with monitoring tools for ML models (e.g., Dynatrace, Prometheus, Grafana, or MLFlow).
Strong understanding of version control for models and data (e.g., Git).
Knowledge in scripting using python/unix bash.

Roles & Responsibilities

Good in communication, coordination and proactive in nature.
Self-driven, customer centric and innovative.
Checking deployment pipelines for machine learning models.
Review Code changes and pull requests from the data science team.
Triggers CI/CD pipelines after code approvals.
Monitors pipelines and ensures all tests pass and model artifacts are generated/stored correctly.
Deploys updated models to prod after pipeline completion.
Works closely with the software engineering and Dev Ops team to ensure smooth integration.
Containerize models using Docker and deploy on cloud platforms (like AWS/GCP/Azure).
Set up monitoring tools to track various metrics like response time, error rates, and resource utilization.
Establish alerts and notifications to quickly detect anomalies or deviations from expected behavior.
Analyze monitoring data, log, files, and system metrics.
Collaborate with the data science team to develop updated pipelines to cover any faults.
Documenting and troubleshoots, changes, and optimization.