Sr. AI Engineer

• 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.

• 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.

• Private medical
• Income protection
• Hybrid working model (3:2)
• Attractive pension scheme
• Positive work life balance
• Learning and development
• Life insurance
• Lenovo and Motorola products discounts
• Lifestyle discounts
• Cycle to work
• MyGymDiscounts
• Mortgage advice and support
• Referral bonus

Why Work at Lenovo

We are Lenovo. We do what we say. We own what we do. We WOW our customers. Lenovo is a US$83 billion revenue global technology powerhouse, ranked #153 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY). This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com, and read about the latest news via our StoryHub., The Advanced AI Technology Center (AAITC)-Lenovo’s global AI Center of Excellence-is driving our transformation into an AI-first organization. We are assembling a world-class team of researchers, engineers, and innovators to position Lenovo and its customers at the forefront of the generational shift toward AI.