Sr. Manager, AI Platform Engineering

LATC is seeking an accomplished Senior Infrastructure & Platform Engineer to take a leadership role within our AI Technology Center. In this senior position, you will architect, build, and evolve the large-scale infrastructure and platforms that underpin software and AI model development, deployment, and operation across the organization. You will serve as a technical authority on infrastructure strategy, mentor junior engineers, drive best practices, and collaborate cross-functionally with research, engineering, and product teams. Your deep expertise will be instrumental in scaling our AI capabilities and ensuring production-grade reliability, performance, and security. If you are passionate about making Smarter Technology for All, come help us realize our Hybrid AI vision! #LATC Responsibilities Infrastructure & Platform Strategy and Architecture: Define and drive the long-term infrastructure strategy. 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, line manager and mentor to your team of platform and infrastructure engineers, establishing engineering standards, conducting design reviews, and fostering a culture of operational excellence across the team. Advanced Automation and CI/CD: Architect and maintain sophisticated automation pipelines for Software development and 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. 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. Experience leading 24/7 managed services and operations teams. 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 proficiency in Python and at least one additional systems language (e.g., Go, C++, Rust, Java). Deep experience with AI-specific hardware and software stacks (e.g., NVIDIA GPUs, CUDA, cuDNN, NCCL, InfiniBand/RoCE networking). Proven track record managing high-performance computing (HPC) environments, including job scheduling, resource allocation, cluster maintenance, and performance 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. 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). Bonus Points Experience with AI and machine learning frameworks at scale (e.g., PyTorch, DeepSpeed, Megatron-LM, vLLM). Contributions to open-source infrastructure or ML tooling projects. Experience with large language model (LLM) training and inference infrastructure. What We can offer You: Holiday purchase Private medical Income protection Attractive pension scheme Positive work life balance Learning and development Hybrid working model (3:

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