AI engineer

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.

• 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.
• 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.
• 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.
• 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.
• 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.
• Lead efforts to profile, benchmark, and optimize AI infrastructure and model serving for throughput, latency, GPU utilization, scalability, and cost efficiency at scale.
• Establish and enforce security best practices, access controls, and compliance frameworks across AI infrastructure. Ensure adherence to relevant regulatory requirements and internal governance policies.
• 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
• Будет плюсом: 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

• Holiday purchase
• 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