Senior Engineer - AI Delivery

Overview

This role is essential in advancing AI innovations from research into live production by implementing and managing AI models to ensure optimal performance, scalability, security, and reliability. It requires close cooperation with engineering, evaluation, infrastructure, and offshore development center (ODC) teams to successfully launch AI solutions that comply with organizational policies.

Primary Responsibilities

• Deploy machine learning (ML) and large language models (LLMs) into production environments utilizing containerization and cloud platforms, following technical guidelines.
• Set and manage deployment parameters such as autoscaling and runtime configurations to assure service availability and scalability.
• Monitor AI solutions in operation, troubleshooting issues that impact model uptime and performance.
• Analyze and enhance AI model efficiency by monitoring latency, error rates, resource use, and infrastructure expenses, applying optimization techniques including caching, pruning, quantization, and cost tuning.
• Create dashboards and alert mechanisms to maintain transparency on model health, resource consumption, and output quality.
• Engineer AI solutions by constructing service APIs and packaging for deployment, integrating them with data sources, identity management, logging, and enterprise systems compliant with company standards.
• Manage model lifecycle operations including version control, model registries, controlled releases, and rollback strategies, supported by unit and integration testing.
• Establish Continuous Integration/Continuous Deployment (CI/CD) pipelines to automate testing and release cycles for AI systems.
• Conduct both automated and human-in-the-loop evaluations to assess model accuracy, reliability, and quality; track data and concept drift to pinpoint retraining requirements; document assessment outcomes detailing risks, gaps, and recommendations.
• Collaborate with infrastructure teams to provision necessary hardware (GPU/CPU), storage, and cloud resources; support integration of vector databases, feature stores, and prompt registries; ensure backup, recovery, resilience, and readiness for production deployment.
• Coordinate with offshore development to ensure alignment with delivery timelines, quality standards, and security policies; review technical outputs and maintain engineering best practices and documentation standards.
• Develop and maintain comprehensive technical documentation, deployment procedures, runbooks, configurations, and operational guidelines to safeguard maintainability and facilitate knowledge transfer across teams.
• Adhere strictly to departmental policies, operating procedures, quality, safety, environmental, and information security standards to uphold a safe, consistent, and secure work environment.

Qualifications and Experience

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Computer Engineering, or a closely related discipline.
• Possession of professional certifications in Cloud Computing, MLOps, DevOps, AI or Machine Learning is advantageous.
• Between 2 to 4 years’ experience in areas relevant to AI/ML engineering, MLOps, software engineering, or AI solution deployment.