AI Engineer Consultant

About the role

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 advantage, transforming mission-critic al operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements

Our Industry Solutions offering provides verticalized solutions that transform how clients sell products, deliver services, generate growth, and execute mission-critical operations. We deliver integrated business expertise with scalable, repeatable technology solutions specifically engineered for each sector.

Qualifications

A successful candidate would possess these skills:

Ability to work independently and collaborate as part of a team

Effective written and verbal communication skills

Meticulous attention to detail and quality of work product

Ability to build and sustain professional relationships

Ability to lead projects or workstreams

Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment

Strong interpersonal skills and professional demeanor

Required

3-6+ years of experience in DevOps/MLOps/Data Engineering, with at least 1-2 years hands-on with Databricks and Azure

Strong proficiency in Python and/or Scala, plus SQL for data transformation and querying

Hands-on experience with Databricks (Jobs, Workflows, Unity Catalog, Delta Lake, Databricks Model Serving)

Proficiency in Azure ecosystem: Azure DevOps, Azure ML, Azure Monitor, Azure Key Vault, Azure Data Factory

Experience with MLflow for experiment tracking and model registry management

Working knowledge of CI/CD practices and Infrastructure as Code (Terraform or ARM/Bicep templates)

Understanding of ML lifecycle concepts - model training, validation, deployment, monitoring, and retraining

Limited immigration sponsorship may be available

Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve. This may include overnight travel.

Bachelor's degree, preferably in Computer Sciences, Information Technology, Computer Engineering, or related IT discipline ; or equivalent experience

Preferred

Familiarity with containerization (Docker) and orchestration (Kubernetes, if applicable)

Analytical ability to manage multiple projects and prioritize tasks into manageable work products

Can operate independently or with minimum supervision

Excellent written and communication skills

Ability to deliver technical demonstrations