SRE - AI ML Support Engineer
18 hours ago
Experience: 6+ years
NP-0 to 30 days
Please find JD:
We are hiring a "SRE [Site Reliability Engineer] AI ML Support" engineer for our "Enterprise-grade highperformance supercomputing" platform. We are helping enterprises and service providers build their AI
inference platforms for end users, powered by our state-of-the-art RDU (Reconfigurable Dataflow Unit)
hardware architecture. Our cloud-agnostic, enterprise-grade MLOps platform abstracts infrastructure
complexity and enables seamless deployment, management, and scaling of foundation model workloads at
production scale. You'll contribute to the core of our enterprise-grade AI platform, collaborating across teams to
ensure our systems are performant, secure, and built to last. This is a high-impact, high-visibility role working
at the intersection of AI infrastructure, enterprise software, and developer experience.
Minimum Requirements:
• Foundational ML knowledge with hands-on experience working with machine learning models,
especially large language models (LLMs) and LLM APIs
• Strong programming skills in Python, including working with ML frameworks (PyTorch, Huggingface,
LangChain, etc) as well as building scripts, automation
• Solid understanding of Generative AI concepts (such as RAG) and applied use cases
• Exposure to Linux systems and familiarity with troubleshooting environment/setup issues
• Ability to investigate, triage, and resolve customer or internal issues related to ML workflows, APIs, and
AI-based applications
• Experience with issue tracking, documentation, and collaboration platforms (e.g., ticketing systems,
project tracking tools, knowledge bases)
• Proficiency with Docker for containerization and shell scripting for system automation
• Good communication and collaboration skills to work with cross-functional teams as well as external
customers or stakeholders
Nice to have:
• Familiarity with multi-modal models (e.g. Llama 4 Maverick)
• Familiarity with ML Ops practices – monitoring, observability, exposure to related libraries and
frameworks like OpenSearch, Prometheus and Grafana
• Strong hands-on exposure to Linux system administration and network administration, including
troubleshooting, system monitoring, and optimizing performance
• Experience working with Kubernetes (on-prem deployments preferred) for managing containerized ML
workloads
• Exposure to one or more public cloud platforms (AWS, GCP, Azure, etc)
• Strong customer-facing communication skills to handle escalations, reliability concerns, and solution
discussions with stakeholders and clients in a B2B environment
Ways to stand out from the crowd:
• Prior experience working with APIs and SDKs of major LLM providers (OpenAI, Anthropic, Hugging
Face, etc)
• Demonstrated ability to resolve complex issues in production ML systems
• Knowledge of fine-tuning, prompt engineering, and optimizing LLM usage in production.
Thanks
Aparna Surnis
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