AI Infrastructure Architect

What Youll Do

• Architect the Future:

  Lead the end-to-end design and development of our AI infrastructure, encompassing hardware, software, networking, and multi-cloud environments

• Innovate and Evaluate:

  Assess, select, and implement the best-in-class technologies, tools, and frameworks (e.g., TensorFlow, PyTorch, Kubernetes, Docker) to build and maintain our AI platforms.

• Optimize for Performance:

  Engineer and implement infrastructure that scales seamlessly with our evolving AI/ML needs, continuously monitoring and optimizing for both performance and cost-efficiency.

• Champion Security and Compliance:

  Define and enforce infrastructure standards and best practices, ensuring unwavering compliance with security policies, data protection regulations, and ethical AI principles.

• Build Data-Driven Pipelines:

  Collaborate on the architecture and implementation of highly efficient data pipelines for our AI models, from ingestion and storage to processing and management.

• Lead and Inspire:

  Provide technical leadership and mentorship to cross-functional teams, fostering a culture of excellence and best practices in AI infrastructure.

• Solve Complex Challenges:

  Diagnose and resolve intricate infrastructure issues, ensuring the high availability and reliability of all our AI systems.

• Stay Ahead of the Curve:

  Keep your finger on the pulse of the latest advancements in AI, machine learning, and cloud computing, driving innovation within the organization.

• Document for Success:

  Create and maintain comprehensive documentation for all AI infrastructure designs, implementations, and operational procedures.

What Youll Bring

Education:

Experience:

• 15+ years of experience in infrastructure architecture.
• A minimum of 3-5 years of dedicated experience in designing and building AI-specific infrastructure.
• Proven success in deploying scalable and secure AI solutions in cloud environments
• Extensive, hands-on experience with containerization and orchestration technologies like Docker and Kubernetes.

Technical Prowess:

• Proficiency with the command line and experience with both cloud-native and on-premise data center deployments.
• A strong understanding of deep learning architectures and the latest developments in Large Language Models (LLMs).
• Expertise in NVIDIA hardware and software, including performance tuning and diagnostics.
• Hands-on experience with GPU systems, including performance testing, tuning, and benchmarking.
• Proficiency in programming languages such as Python.
• A deep understanding of cloud service models (IaaS, PaaS, SaaS) and cloud-native architectures.
• In-depth knowledge of networking, storage, and security best practices in a cloud context.
• Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
• Familiarity with DevOps and MLOps principles and practices.

Soft Skills:

• Exceptional problem-solving and analytical abilities with a data-driven approach.
• Excellent communication and interpersonal skills, capable of articulating complex technical concepts to diverse audiences.
• Proven ability to lead, mentor, and collaborate effectively within a team environment.
• A strategic mindset with the ability to align technical solutions with overarching business goals.
• A proactive, adaptable, and continuous learner who thrives in a dynamic technological landscape.