Role: Senior LLMOps / MLOps Engineer Location: Santa Clara, CA (Onsite) Duration: 6-12+ Months Contract
Only Local Candidates will work out
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
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 ContinuoDynamic 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.
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