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 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 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

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