MLOps Engineer

MLOps Engineer

Location

ITPL, Bengaluru

work mode

4 days work from office

Interview

AMAT interview will face to face - need local candidates

Experience Required

5 years

Salary

24 LPA

JLT00003 - MLOps Engineer

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