MLOps Engineer
First seen by Alion on Aug 22, 2026.
Role : MLOps Engineer (JLT).
Location : Bangalore (Working from Office / Hybrid).
Job Description : 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 :
- 58 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 :
1. Kubernetes
2. Databricks
3. GPU Infrastructure
- Experience with :
1. vLLM
2. Triton Inference Server
3. Ray Serve
4. SGLang
5. 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.
Skills
MLOps, LLM, AIOps, Kubernetes, Azure Databricks, Python, PyTorch, GPU, Azure, VectorDB, OpenShift