MLOps Engineer

Dice is the leading career destination for tech experts at every stage of their careers. Our client, HCL Global Systems, is seeking the following. Apply via Dice today!

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 Dev Ops/Git Lab/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 Dev Ops/Git Lab/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

• Executes relevant data wrangling activities related to the problem
• Conduct ML experiments to understand feasibility; building baseline models to solve the business problem
• Fine tune the baseline model for optimum performance
• Test Models internally per acceptance criteria from the business
• Identify areas and techniques to optimize the model based on test results
• Document relevant artefacts for communicating with the business
• Work with data scientists to deploy the models.
• Work with product teams in planning and execution of new product releases.
• Set OKRs and success steps for self/ team and provide feedback of goals to team members
• Identify metrics for validating the models and communicate the same in business terms to the product teams.
• Keep track of the trends and do rapid prototyping to understand the feasibility of utilizing in existing solutions