Job DescriptionWe are seeking a Senior AI Engineer specializing in MLOps and AIOps, who will play a critical role in deploying, operating, and optimizing enterprise-grade AI solutions built on Azure, collaborating closely with AI Developers, Product Owners, Governance leaders and Cloud Architects to ensure these systems are reliable, scalable, and cost-effective across tax, audit, advisory, and client-facing applications. This role goes beyond traditional DevOps-it's about engineering AI into the fabric of enterprise operations, enabling secure, observable, and governed machine learning deployments that deliver real business impact.
What Work You Will be Responsible For: - Deploy and monitor AI models across Azure services with robust telemetry for performance, drift, and availability.
- Manage model upgrades including APIs and UIs with structured rollout, version control, and rollback support.
- Optimize performance and cost through testing, profiling, and tuning of inference infrastructure and pipelines.
- Implement MLOps pipelines for continuous integration, deployment, and lifecycle management using Azure ML and GitHub Actions.
- Ensure compliant change management for all AI-related deployments, with auditability, security, and governance controls.
Basic Qualifications- 5-7+ years in MLOps and/or AIOps, ideally within Azure cloud environment with hands-on experience with Azure ML, Synapse, Data Lake, App Services, Cosmos DB, and Azure AI Foundry
Preferred/Desired Qualifications- Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field preferred.
- Consulting background with a strong bias for action.
- Workflow Design: Prompt flow, automation pipelines, and human-in-the-loop systems
- Post-Training Techniques: Fine-tuning, instruction tuning, RLHF, and domain adaptation
- Azure DevOps, App Insights, Log Analytics, Key Vault, and Managed Identity integration.
- Tools for inference performance testing and profiling (e.g., locust, K6, or custom scripts).
- Model Evaluation: Performance metrics, benchmark development, and A/B testing frameworks
- Understanding of model observability, telemetry, and incident response for AI systems.
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Preferred Location:Iselin
