What are the responsibilities and job description for the MLOps Engineer position at HCL Global Systems?
Job Details
Role : MLOps Engineer
Alpharetta GA (or) Berkeley Heights NJ
days onsite - 3 -5 days
Must Have Skills
Skill 1 End-to-End ML lifecycle management (Azure ML, Databricks, MLflow)
Skill 2 Monitoring production model serving & data pipelines (Docker, AKS/Kubernetes, Databricks/Spark, Feature Stores)
Skill 3 MLOps CI/CD and observability (Azure DevOps/GitLab/Harness)
Skill 4 Monitoring & drift detection (Splunk, Azure Monitor, Dynatrace, Prometheus)
Good To Have Skills
Skill 1 Autoscaling and API gateway integration
Skill 2 SLA-backed delivery
Skill 3 Advanced deployment strategies (blue-green, canary)
Skill 4 Feature store design and optimization
Job Description:
Hands-on experience with End-to-end ML lifecycle management with Azure ML, Databricks, and MLflow (experiment tracking, model versioning/registry, dev/test/prod promotion, reproducible builds).
Comprehensive knowledge of monitoring production model serving and data pipelines using Docker and AKS/Kubernetes with Databricks/Spark and Feature Stores; autoscaling, API gateway integration, and SLA-backed delivery
Proven track record in designing, implementing, and managing MLOps CI/CD and observability: Azure DevOps/GitLab/Harness with validation gates (unit/integration/offline online checks), canary/blue green and rollback; monitoring and drift detection via Splunk/Azure Monitor/Dynatrace/Prometheus.
Design and develop ML solutions, that will enable intelligent experiences and provide value. Collaboratively work with business, technology, and product teams to understand the product objectives and formulate the ML problem, under minimal guidance from Lead II
1. Executes relevant data wrangling activities related to the problem
2. Conduct ML experiments to understand feasibility; building baseline models to solve the business problem
3. Fine tune the baseline model for optimum performance
4. Test Models internally per acceptance criteria from the business
5. Identify areas and techniques to optimize the model based on test results
6. Document relevant artefacts for communicating with the business
7. Work with data scientists to deploy the models.
8. Work with product teams in planning and execution of new product releases.
9. Set OKRs and success steps for self/ team and provide feedback of goals to team members
10. Identify metrics for validating the models and communicate the same in business terms to the product teams.
11. Keep track of the trends and do rapid prototyping to understand the feasibility of utilizing in existing solutions