Posted 29 July, 2026
MLOps Engineer
Tranzeal Inc.
Bengaluru, Karnataka, IN
Full Time
Reference: 26-00784-1238-1
Senior MLOps Engineer
Experience: 5 Years
Salary: 24.5 LPA
Location: ITPL, BLR
Work Mode: 5 days from office
We are seeking a hands-on AI Deployment Engineer specializing in ML Engineering, Model Deployment, Model Governance, and Model Observability. The engineer will own the complete lifecycle of Deep Learning models, LLMs, and SLMs across cloud, on-premises, hybrid, and air-gapped environments.
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
" 3 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
Good-to-Have Skills
" Kafka, RabbitMQ, Event Hub
" Fine-tuning and model optimization
" Model Governance & Security
" Experience with Llama, Mistral, DeepSeek, Qwen, Phi, and Gemma models
Experience: 5 Years
Salary: 24.5 LPA
Location: ITPL, BLR
Work Mode: 5 days from office
We are seeking a hands-on AI Deployment Engineer specializing in ML Engineering, Model Deployment, Model Governance, and Model Observability. The engineer will own the complete lifecycle of Deep Learning models, LLMs, and SLMs across cloud, on-premises, hybrid, and air-gapped environments.
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
" 3 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
Good-to-Have Skills
" Kafka, RabbitMQ, Event Hub
" Fine-tuning and model optimization
" Model Governance & Security
" Experience with Llama, Mistral, DeepSeek, Qwen, Phi, and Gemma models
