Senior LLMOps / MLOps Engineer

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.