SRE - AI ML Support Engineer

7 days ago

Pune, India Emergys Full time

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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