Posted 15 August, 2026
MLOPS
Diverse Lynx
Bangalore
Full Time
Reference: 365_569689_26-03166
Scope of Work
• Build and manage MLOps and LLMOps pipelines.
• Deploy, host, and scale Deep Learning models, LLMs, and SLMs and Inference optimisation
• Manage end-to-end model lifecycle including versioning, deployment, rollout, rollback, and retirement.
• Host models on Databricks, Kubernetes, OpenShift, and GPU-based infrastructure.
• Implement model governance, lineage, approval workflows, and compliance controls.
• Build model monitoring, observability, tracing, logging, and drift detection capabilities.
• Optimize model performance, latency, throughput, GPU utilization, and cost.
• Support cloud, on-premises, hybrid, and air-gapped environments.
Must-Have Skills
• 5+ years in MLOps, LLMOps, ML Engineering, or AI Engineering.
• Strong Python development skills.
• Hands-on experience with Databricks and/or Azure ML.
• Experience with Deep Learning, LLMs, SLMs, RAG, and Hugging Face.
• Experience deploying models built using PyTorch and TensorFlow.
• Strong expertise in model deployment on:
o Kubernetes
o Databricks
o GPU Infrastructure
• Experience with:
o vLLM
o Triton Inference Server
o Ray Serve
o SGLang
o Databricks Model Serving
• Strong GPU knowledge including NVIDIA GPUs, CUDA, multi-GPU deployments, and inference optimization.
• Experience in Model Registry, Model Governance, Model Monitoring, Drift Detection, and AI Observability.
• Strong database knowledge (SQL Server, PostgreSQL, Oracle, MySQL, MongoDB).
• Experience with Vector Databases (Pinecone, Chroma, FAISS, Milvus, Azure AI Search).
• REST APIs, WebSockets, Streaming HTTP.
• Experience with MLflow, OpenTelemetry, LangFuse, Splunk, and Grafana/ELK.
• CI/CD using Jenkins, Azure DevOps.
• Experience across Cloud, On-Premises, Hybrid, and Air-Gapped environments.
• Experience with Auth setup like Keycloak
• Build and manage MLOps and LLMOps pipelines.
• Deploy, host, and scale Deep Learning models, LLMs, and SLMs and Inference optimisation
• Manage end-to-end model lifecycle including versioning, deployment, rollout, rollback, and retirement.
• Host models on Databricks, Kubernetes, OpenShift, and GPU-based infrastructure.
• Implement model governance, lineage, approval workflows, and compliance controls.
• Build model monitoring, observability, tracing, logging, and drift detection capabilities.
• Optimize model performance, latency, throughput, GPU utilization, and cost.
• Support cloud, on-premises, hybrid, and air-gapped environments.
Must-Have Skills
• 5+ years in MLOps, LLMOps, ML Engineering, or AI Engineering.
• Strong Python development skills.
• Hands-on experience with Databricks and/or Azure ML.
• Experience with Deep Learning, LLMs, SLMs, RAG, and Hugging Face.
• Experience deploying models built using PyTorch and TensorFlow.
• Strong expertise in model deployment on:
o Kubernetes
o Databricks
o GPU Infrastructure
• Experience with:
o vLLM
o Triton Inference Server
o Ray Serve
o SGLang
o Databricks Model Serving
• Strong GPU knowledge including NVIDIA GPUs, CUDA, multi-GPU deployments, and inference optimization.
• Experience in Model Registry, Model Governance, Model Monitoring, Drift Detection, and AI Observability.
• Strong database knowledge (SQL Server, PostgreSQL, Oracle, MySQL, MongoDB).
• Experience with Vector Databases (Pinecone, Chroma, FAISS, Milvus, Azure AI Search).
• REST APIs, WebSockets, Streaming HTTP.
• Experience with MLflow, OpenTelemetry, LangFuse, Splunk, and Grafana/ELK.
• CI/CD using Jenkins, Azure DevOps.
• Experience across Cloud, On-Premises, Hybrid, and Air-Gapped environments.
• Experience with Auth setup like Keycloak
