What are the responsibilities and job description for the Senior LLMOps / MLOps Engineer position at Nityo Infotech Corporation?
We are hiring for Senior LLMOps / MLOps Engineer (5-7 Years) at Santa Clara, CA (Onsite)
Senior LLMOps / MLOps Engineer (5-7 Years) at Santa Clara, CA (Onsite)
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.