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.

Qualifications 

• 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.