Job description

Role: Senior LLMOps / MLOps Engineer(23382-1) Location: Santa Clara, CA Onsite Requirement – Yes Number of days onsite – 5 days Must Have Skills Skill 1 – Strong proficiency in Python and software engineering best practices Skill 2 – 14+ years of experience in MLOps, LLMOps, AI/ML Platform Engineering Skill 3 – Strong expertise in LLM Inferencing and Model Hosting using vLLM, SGLang, TGI, Triton, Ray Serve, Azure ML, or Databricks Model Serving Good To have Skills – Skill 1 – Exposure to AI Observability, Governance, and Responsible AI practices Mandatory if Applicable Domain Experience (If any) – Senior LLMOps / MLOps Engineer Summary We are looking for a highly skilled Senior LLMOps / MLOps Engineer with strong expertise in LLM inferencing, model hosting, and serving Large Language Models (LLMs) at scale. The ideal candidate should be a hands-on engineer with proven experience deploying and optimizing open-source LLMs, building high-performance inference platforms using technologies such as vLLM, SGLang, TGI, Triton, and Ray Serve, and driving GPU utilization, latency, throughput, and cost optimization. This is a highly technical role requiring active involvement in designing, building, troubleshooting, and optimizing production AI systems. Experience in MLOps platforms and scalable AI infrastructure is essential. Must-Have Skills • 5-7 years of experience in MLOps, LLMOps, AI/ML Platform Engineering. • Strong proficiency in Python and software engineering best practices. • Experience working with open-source LLMs such as Llama, Mistral, Gemma, or Qwen. • Strong expertise in LLM Inferencing and Model Hosting using vLLM, SGLang, TGI, Triton, Ray Serve, Azure ML, or Databricks Model Serving. • Experience with Kubernetes, Docker, Azure ML, Databricks, and MLflow. • Good understanding of RAG, Vector Databases, GPU Optimization, Quantization, KV Cache, PagedAttention, and Continuous/Dynamic Batching. • Demonstrated hands-on experience building, deploying, troubleshooting, and optimizing production-grade LLM and GenAI solutions. • Experience deploying, scaling, and monitoring production-grade GenAI/LLM applications. • Exposure to AI Observability, Governance, and Responsible AI practices. Good-to-Have Skills • Hands-on experience with LLM Fine-Tuning using PEFT, SFT, CPT, LoRA, and QLoRA techniques. • Experience with Azure AI Foundry, Azure OpenAI, Hugging Face, DeepSpeed, and PEFT. • Knowledge of distributed training and multi-GPU environments. • Experience with Agentic AI frameworks such as LangGraph, AutoGen, or CrewAI. • Understanding of simulation platforms, digital twins, modeling & simulation workflows, or scientific computing.

Requirements

Must-haves

Role: Senior LLMOps / MLOps Engineer(23382-1)
Location: Santa Clara, CA
Onsite Requirement – Yes
Number of days onsite – 5 days
Must Have Skills
Skill 1 – Strong proficiency in Python and software engineering best practices
Skill 2 – 14+ years of experience in MLOps, LLMOps, AI/ML Platform Engineering
Skill 3 – Strong expertise in LLM Inferencing and Model Hosting using vLLM, SGLang, TGI, Triton, Ray Serve, Azure ML, or Databricks Model Serving
Good To have Skills –
Skill 1 – Exposure to AI Observability, Governance, and Responsible AI practices
Mandatory if Applicable
Domain Experience (If any) – Senior LLMOps / MLOps Engineer
Must-Have Skills
• 5-7 years of experience in MLOps, LLMOps, AI/ML Platform Engineering.
• Strong proficiency in Python and software engineering best practices.
• Experience working with open-source LLMs such as Llama, Mistral, Gemma, or Qwen.
• Strong expertise in LLM Inferencing and Model Hosting using vLLM, SGLang, TGI, Triton, Ray Serve, Azure ML, or Databricks Model Serving.
• Experience with Kubernetes, Docker, Azure ML, Databricks, and MLflow.
• Good understanding of RAG, Vector Databases, GPU Optimization, Quantization, KV Cache, PagedAttention, and Continuous/Dynamic Batching.
• Demonstrated hands-on experience building, deploying, troubleshooting, and optimizing production-grade LLM and GenAI solutions.
• Experience deploying, scaling, and monitoring production-grade GenAI/LLM applications.
• Exposure to AI Observability, Governance, and Responsible AI practices.
Good-to-Have Skills
• Hands-on experience with LLM Fine-Tuning using PEFT, SFT, CPT, LoRA, and QLoRA techniques.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

pythonaimlllmazurekubernetesdocker

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