
MLops Engineer
7 days ago
Job Description:
- Design, develop, and maintain
end-to-end MLOps pipelines
for training, testing, and deploying ML models at scale. - Implement and optimize
CI/CD workflows using GitHub Actions
for ML model integration and delivery. - Work extensively on
Databricks
for model training, orchestration, and deployment. - Automate data ingestion, feature engineering, and ML model retraining workflows.
- Monitor model performance in production and enable automated re-training or alerting mechanisms.
- Experience with code versioning tools like GitHub, Gitlab, SVC, etc.
- Ensure ML systems follow
best practices in version control, reproducibility, and scalability
. - Collaborate with cross-functional teams (Data Scientists, Engineers, Product Managers) to align business objectives with ML solutions.
- Implement security, compliance, and governance standards for ML pipelines.
Must-Have Skills
- Databricks
(MLflow, Delta Lake, Model Registry, Unity Catalog). - GitHub CI/CD pipelines
(bare minimum requirement). - Proficiency in
Python
and ML-related frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Strong understanding of
cloud platforms
(AWS / Azure / GCP) with ML services integration. - Experience with
containerization & orchestration
(Docker, Kubernetes). - Knowledge of
data engineering workflows
and distributed data processing (Spark preferred).
Good-to-Have Skills
- Experience with
Terraform / Infrastructure as Code (IaC)
for environment setup. - Monitoring tools (Prometheus, Grafana, Datadog, or equivalent).
- Knowledge of
feature stores
and real-time inference pipelines. - Exposure to
data governance, security, and compliance frameworks
.
Qualifications
- Bachelor's or master's degree in computer science, Data Engineering, or a related field.
- 3–6 years of experience in MLOps, DevOps, or Data Engineering with ML focus.
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