AI Engineer Consultant

Are you an experienced, passionate pioneer in technology who wants to work in a collaborative environment? As an experienced AI Engineer Consultant you will have the ability to share new ideas and collaborate on projects as a consultant without the extensive demands of travel.

If so, consider an opportunity with Deloitte under our Project Delivery Talent Model.

Project Delivery Model (PDM) is a talent model that is tailored specifically for long-term, onsite client service delivery.

Work you'll do/Responsibilities

As an AIOps/MLOps Engineer Consultant, you will be working in an Azure + Databricks environment:

• Monitor Databricks jobs and clusters - track job run status, cluster utilization, and auto-scaling behavior via Databricks Jobs UI and Azure Monitor, proactively resolving failed or delayed pipeline runs.

• Manage CI/CD pipelines using Azure DevOps - build and maintain automated pipelines (YAML-based) for deploying notebooks, ML models, and Databricks workflows across dev/staging/prod environments using Databricks Repos and Git integration.

• Operate MLflow for model lifecycle management - track experiments, register models in the MLflow Model Registry, manage staging/production transitions, and maintain versioning and lineage.

• Maintain Delta Lake pipelines - ensure data quality, schema enforcement, and ACID compliance across bronze/silver/gold layers feeding into training and inference workloads.

• Monitor model performance and drift - set up automated drift detection (data/concept drift) using Databricks' native monitoring or custom Azure ML integration, triggering retraining pipelines when thresholds are breached.

• Manage compute and cost optimization - configure and right-size Databricks clusters (job clusters vs.

all-purpose), leverage autoscaling and spot instances, and monitor Azure cost management dashboards to control spend.

• Implement observability with Azure Monitor & Log Analytics - set up end-to-end logging/alerting across Databricks, Azure ML, and downstream services using Azure Monitor, Application Insights, and Log Analytics workspaces.

• Manage security, access, and governance - configure Unity Catalog for data/model governance, manage service principals, secrets (via Azure Key Vault), and RBAC across workspaces.

• Collaborate on model deployment via Azure ML endpoints - deploy models as real-time or batch endpoints (Azure ML Managed Endpoints or Databricks Model Serving), ensuring scalability and low-latency inference.

• Handle on-call support and incident response - troubleshoot pipeline failures, cluster crashes, or endpoint downtime, using root cause analysis and post-incident reviews to improve pipeline resilience.

The Team

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/ver ticalized sector solutions in software, data, AI, network, and hybrid cloud i nfrastructure.

These solutions are powered by engineering for business...