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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. AI Engineer - **Company:** Lenovo - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Multitier Architecture, A/B Testing, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Bash Shell, C++ (Programming Language), Cloud Computing, Computer Clusters, Computer Engineering, System Configuration, Continuous Integration, Software Design Patterns, DevOps, File Systems, Distributed Systems, Monitoring of Systems, Python (Programming Language), Linux System Administration, Machine Learning, Open Source Technology, Package Management Systems, Ansible, Prometheus, Shell Script, Software Engineering, AI Infrastructure, Rust (Programming Language), Datadog, Pulumi, Graphics Processing Unit (GPU), Google Cloud, Pytorch, DeepSpeed, Large Language Models, Grafana, Kubernetes Helm Charts, HybridCloud, Containerization, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, Machine Learning Operations, VLLM, Terraform, Megatron, OpenSearch, Docker - **Published:** October 26, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=c6568296fced0661 ## About the Role * Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field. Advanced degree preferred. * Proficient in general mathematics & statistics . * 12+ years of experience in software engineering, infrastructure engineering, DevOps/SRE, or a related field, with at least 4 years focused on AI/ML infrastructure. * Demonstrated experience leading or mentoring engineering teams on infrastructure projects. * Deep expertise in computer systems, distributed systems, and cloud computing architectures. * Strong proficiency in Python and at least one additional systems language (e.g., Go, C++, Rust, Java) as well as understanding of Design Patterns and Solution Architecture. * Expert-level Linux system administration, including package management, user/group management, file system internals, shell scripting (e.g., Bash), networking, and system configuration (e.g., systemd, kernel tuning). * Strong architectural thinking with the ability to balance trade-offs across performance, reliability, security, and cost. * Excellent communication and collaboration skills, with the ability to influence technical decisions across teams and present to senior leadership. * Ability to thrive in a fast-paced, ambiguous environment and drive clarity through technical leadership. * Expertise with observability stacks (e.g., Prometheus, Grafana, ELK/OpenSearch, Datadog) for infrastructure, open telemetry n-tier application stacks and model monitoring with open telemetry and open inference. * Knowledge of observability stacks, open Bonus Points * Experience with AI and machine learning frameworks at scale (e.g., PyTorch, DeepSpeed, Megatron-LM, vLLM). * Hands-on experience with major cloud platforms (e.g., AWS, GCP, Azure) and hybrid cloud architectures. * Advanced experience with containerization (Docker) and orchestration (Kubernetes), including custom operators, Helm charts, and GPU scheduling plugins. * Expertise with observability stacks (e.g., Prometheus, Grafana, ELK/OpenSearch, Datadog) for infrastructure and model monitoring. * Experience with infrastructure-as-code tools (e.g., Terraform, Ansible, Pulumi). * Contributions to open-source infrastructure or ML tooling projects. * Experience with large language model (LLM) training and inference infrastructure. ## Description * AI Infrastructure Strategy and Architecture: Define and drive the long-term infrastructure strategy for AI workloads. Architect scalable, cost-efficient, and resilient compute, storage, and networking solutions that support the full AI lifecycle from experimentation through production. * Technical Leadership and Mentorship: Serve as a technical lead and mentor to infrastructure engineers, establishing engineering standards, conducting design reviews, and fostering a culture of operational excellence across the team. * AI Model Deployment and Platform Engineering: Design and own the platforms, frameworks, and processes for deploying, monitoring, and managing AI models at scale in production environments, including model serving, A/B testing infrastructure, and rollback mechanisms. * Advanced Automation and CI/CD: Architect and maintain sophisticated automation pipelines for AI model training, evaluation, testing, and deployment. Champion infrastructure-as-code practices and drive continuous improvement of CI/CD workflows. * Cross-Functional Collaboration: Partner with data scientists, ML engineers, product managers, and leadership to align infrastructure capabilities with research and business objectives. Act as the primary infrastructure point of contact for cross-team initiatives. * Performance Engineering: Lead efforts to profile, benchmark, and optimize AI infrastructure and model serving for throughput, latency, GPU utilization, scalability, and cost efficiency at scale. * Security, Compliance, and Governance: Establish and enforce security best practices, access controls, and compliance frameworks across AI infrastructure. Ensure adherence to relevant regulatory requirements and internal governance policies. * Capacity Planning and Cost Management: Own capacity forecasting, resource planning, and cost optimization for GPU clusters and associated infrastructure, balancing performance needs with budget constraints.