Senior Engineer - AI Delivery

Role Purpose

AI Deployment and Operations

• Deploy ML and LLM models into production environments using containers and/or cloud services.
• Configure deployment parameters, autoscaling, and runtime settings for stable, scalable services.
• Monitor deployments and help resolve issues affecting model availability and performance.
• Optimize model performance, latency, error rates, resource use, and infrastructure costs.
• Configure dashboards and alerts for model health, resource consumption, and output quality.

Engineering and Lifecycle

• Develop AI solutions based on models and capabilities produced by GenAI and research teams.
• Build service layers and APIs around AI models, and package solutions for deployment.
• Integrate solutions with data sources, identity services, logging, and enterprise systems.
• Support model registries, versioning, controlled releases, and safe rollback.
• Develop unit and integration tests and CI/CD pipelines for reliable releases.

Evaluation and Production Readiness

• Assess model accuracy, reliability, and output quality using automated and human-in-the-loop evaluations.
• Monitor data and concept drift and help identify model retraining or improvement needs.
• Coordinate provisioning of GPU/CPU, storage, networking, and cloud resources for AI workloads.
• Support vector databases, feature stores, model or prompt registries, backup, recovery, and resilience.

Delivery and Documentation

• Coordinate development, testing, and delivery with ODC teams and review their deliverables against technical and security standards.
• Maintain engineering practices, templates, and technical documentation across teams and centers.
• Prepare deployment guides, runbooks, configurations, operational procedures, and solution architecture documentation.
• Support knowledge transfer to engineering, infrastructure, and operational teams.

Qualifications and Experience

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Computer Engineering, or a related field.
• 2–4 years of relevant experience in AI/ML engineering, MLOps, software engineering, AI solution deployment, or related fields.
• Professional certifications in Cloud Computing, MLOps, DevOps, Artificial Intelligence, or Machine Learning are advantageous.

Policies and Security