Senior Engineer - AI Delivery

Overview Job Description Job Title Senior Engineer Job Code 476244 Grade I2 Group Division Research & AI Department Applied Research Unit Delivery ROLE PURPOSE The aim is to state the overall significance of the job from the organization’s perspective. The role exists to support the transition of AI solutions from research and development into production by deploying and integrating AI models, monitoring their performance, and optimizing the required resources to ensure reliability, scalability, and operational efficiency. The role collaborates with Engineering, Evaluation, Infrastructure, and ODC teams to deliver secure, stable, and policy-compliant AI solutions. Key Accountabilities & Activities This section describes the principal outputs required from the job. Key Accountabilities Key Activities • AI Model Deployment & Operations • Deploy ML and LLM models into production environments using containers and/or cloud services in line with approved technical requirements. • Configure deployment parameters, autoscaling, and runtime settings to support stable and scalable AI services. • Monitor production deployments and support resolution of operational issues affecting model availability and performance. • AI Performance & Resource Optimization • Monitor and optimize AI model performance, latency, error rates, resource utilization, and associated infrastructure costs. • Implement appropriate optimization techniques such as caching, pruning, quantization, resource quotas, and cost tuning. • Configure dashboards and alerts to provide visibility into model health, resource consumption, and output quality. • AI Solution Engineering & Integration • 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 using appropriate engineering frameworks. • Integrate AI solutions with required data sources, identity services, logging capabilities, and enterprise systems in line with Elm standards. • Model Lifecycle & Release Management • Support model lifecycle management through model registries, versioning, controlled releases, and safe rollback mechanisms. • Develop and maintain unit and integration tests to support reliable solution releases. • Establish and maintain CI/CD pipelines to automate testing, deployment, and release activities for AI solutions. • AI Evaluation & Validation • Conduct automated and human-in-the-loop assessments to evaluate AI model accuracy, reliability, and output quality against agreed metrics. • Monitor data and concept drift and support identification of model retraining or improvement requirements. • Prepare evaluation results highlighting identified risks, limitations, performance gaps, and recommended actions. • AI Infrastructure & Production Readiness • Coordinate with infrastructure teams to provision required GPU/CPU, storage, networking, and cloud resources for AI workloads. • Support the setup and integration of enabling technologies such as vector databases, feature stores, and model or prompt registries. • Support backup, recovery, resilience, and production-readiness requirements for deployed AI solutions. • Technical Delivery & ODC Collaboration • Coordinate with ODC teams on development, testing, and technical delivery activities in accordance with agreed delivery and quality requirements. • Review code, technical artifacts, and deliverables received from ODC teams to verify alignment with Elm’s technical and security standards. • Support consistent engineering practices, templates, and technical documentation across delivery teams and centers. • Technical Documentation & Knowledge Transfer • Develop and maintain technical documentation, deployment guides, runbooks, configurations, and operational procedures for AI solutions. • Document solution architecture, dependencies, configurations, and known operational considerations to support maintainability and continuity. • Support knowledge transfer to relevant engineering, infrastructure, and operational teams to enable effective solution support. • Policies, Processes & Procedures • Follow all relevant departmental policies, processes, standard operating procedures, and instructions so that work is carried out in a controlled and consistent manner. • Comply with all relevant safety, quality, and environmental management policies, procedures, and controls to ensure a healthy and safe work environment. • Information Security • Comply with all relevant information security practices and standards to ensure data integrity and confidentiality. JOB SPECIFICATIONS Academic And Professional Qualifications • Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Computer Engineering, or a related field. • Professional certifications in Cloud Computing, MLOps, DevOps, Artificial Intelligence, or Machine Learning are considered an advantage. Years And Nature Of Experience • 2–4 years of relevant experience in AI/ML engineering, MLOps, software engineering, AI solution deployment, or related fields. Show more Show less

Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Computer Engineering, or a related field. Professional certifications in Cloud Computing, MLOps, DevOps, Artificial Intelligence, or Machine Learning are considered an advantage.

AI Model Deployment & Operations: Deploy ML and LLM models into production environments using containers and/or cloud services; Configure deployment parameters, autoscaling, and runtime settings; Monitor production deployments and support resolution of operational issues. AI Performance & Resource Optimization: Monitor and optimize AI model performance, latency, error rates, resource utilization, and infrastructure costs; Implement optimization techniques such as caching, pruning, quantization, resource quotas, and cost tuning; Configure dashboards and alerts. AI Solution Engineering & Integration: Develop AI solutions based on models from GenAI and research teams; Build service layers and APIs; Integrate AI solutions with data sources, identity services, logging, and enterprise systems. Model Lifecycle & Release Management: Support model lifecycle management with registries, versioning, controlled releases, and rollback; Develop tests; Establish CI/CD pipelines. AI Evaluation & Validation: Conduct automated and human-in-the-loop assessments; Monitor data and concept drift; Prepare evaluation results with risks and recommendations. AI Infrastructure & Production Readiness: Coordinate with infrastructure teams to provision GPU/CPU, storage, networking, and cloud resources; Support setup of vector databases, feature stores, and model/prompt registries; Support backup, recovery, resilience, and production-readiness. Technical Delivery & ODC Collaboration: Coordinate with ODC teams on development, testing, and delivery; Review code and artifacts for alignment with Elm standards; Promote consistent engineering practices and documentation. Technical Documentation & Knowledge Transfer: Develop documentation, deployment guides, runbooks, configurations, and operational procedures; Document architecture and dependencies; Transfer knowledge to engineering, infrastructure, and operations. Policies, Processes & Procedures: Follow departmental policies and procedures; Ensure safety, quality, and environmental controls. Information Security: Comply with information security practices and standards for data integrity and confidentiality.