SME AI Cloud

4 weeks ago

Mumbai, Maharashtra, India Yotta Data Services Private Limited Full time
Job Scope:

This is a full-time on-site role as an AI Cloud Engineering SME, as a member of the Yotta IT Engineering team located in Panvel, you will be responsible for evaluating, developing, GPU Supercomputing clusters and networking infrastructure based on NVIDIA reference architecture, infrastructure, platforms, and applications. Design, Evaluate, Enhance, Automate, related to systems on AI platform you will also be responsible for providing technical leadership to the team and working collaboratively with cross-functional teams to identify business opportunities and provide innovative services in the market, to keep the first mover advantage.

Job Responsibilities:

- Provides technical expertise in IT for AI Cloud, (NVIDIA, HPC) systems (Servers, Applications, Cloud, Tools, Automation) development of program requirements relating to information technology, application analysis, software development, and systems integration.
- Designs, plans, implements, handover service or platforms.

Collaborate with NVIDIA Solution Architect & Engineering teams on large-scale GPU-as-a-service projects, both on-premises and in cloud deployments.

- Provides specifications and detailed schematics for system architecture.
- Provides specific detailed information for hardware and software selection, implementation techniques and tools for the most efficient solution to meet business needs, including present and future capacity requirements.
- Desings and implements and optimize software stacks including MaaS (metal-as-a-service), Job Scheduler (SLURM/PBS), Cloud Orchestration (Kubernetes), and Network Management (NetQ for Ethernet fabric and UFM for InfiniBand).
- Well versed with NVIDIA platform both hardware and software layer. Including following
- H100, L40, A100, LMPerf, Pytorch, tensorflow, nemo, LLM models
- UFM, BCM, Kubernetes, NVCF, SLURM
- Conducts testing of system design.
- Evaluates and reports on new technologies to enhance capabilities of the existing systems.
- Ensures the quality of the work produced maintains the highest standards.
- Communicated with cross functional teams to better functioning.
- Participates in the development of the technical solution through the mandated processes.
- Participate in program readiness and design reviews for marquee customers
- Attend technical and programmatic meetings with customers, system users, development team members and represent the organization in matters pertaining to the project.

Participates in the Engineering Review Board for technical review of Engineering Change Requests

Must-Have Skill:

- Hands-on experience with NVIDIA GPU & Networking Technologies, particularly NVIDIA Data Centre GPUs (A100/H100/L40) and NVIDIA Networking Technologies (InfiniBand, Ethernet).
- Proficiency in provisioning and managing software stacks like MaaS, Job Scheduler (SLURM/PBS), Cloud Orchestration (Kubernetes), and Network Management (NetQ for Ethernet fabric and UFM for InfiniBand).
- Prior experience collaborating with NVIDIA Solution Architect & Engineering teams on large-scale GPU-as-a-service projects.
- Familiarity with benchmarking applications from widely-used platforms and frameworks, including MLPerf, PyTorch, TensorFlow, NeMo, Megatron-LM, TensorRT-LLM, Triton Inference Server, and vLLM.
- Experience in performance engineering, including debugging, profiling, benchmarking, and tuning various GPU applications on large-scale supercomputing clusters.

Good to Have Skill:

- Knowledge of other HPC technologies and architectures beyond NVIDIA, broadening expertise in the field.
- Experience with other cloud platforms and orchestration tools, expanding versatility in deployment environments.
- Strong problem-solving and troubleshooting abilities, enabling quick resolution of complex technical issues.
- Excellent communication and collaboration skills to work effectively within cross-functional teams and with external partners.

Behavioral Attributes:

- Strong problem-solving skills with a proactive and solution-oriented approach.
- Excellent communication and collaboration skills for effective customer support.
- Adaptability to handle a dynamic and fast-paced cloud administration environment.
- Commitment to security best practices and continuous improvement.

Qualification and Experience:

- Bachelor's or master's degree in computer science, Engineering, or equivalent industry experience.
- 10 to 15+ years of relevant experience in HPC engineering roles, with a focus on NVIDIA GPU and Networking Technologies.
- Demonstrated success in deploying and managing large-scale GPU Supercomputing clusters, preferably in collaboration with NVIDIA teams.
- Proven track record of performance engineering activities and optimizing GPU applications for high-performance computing workloads.

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