Role: MLOps Engineer (JLT)

Location: Bangalore (Working from Office / Hybrid)

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

Solid 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.

Robust 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

Positive-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

📌 Mlops Engineer Bengaluru
🏢 GyanSys
📍 Bengaluru