Role Description
ML Engineer I
Who We Are
At UST, we help the world’s best organizations grow and succeed through transformation. Bringing together the right talent, tools, and ideas, we work with our client to co-create lasting change. Together, with over 30,000 employees in 30+ countries, we build for boundless impact—touching billions of lives in the process. Visit us at .
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
3–5 years in MLOps, LLMOps, ML Engineering, or AI Engineering.
Solid 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:
Kubernetes
Databricks
GPU Infrastructure
Experience With
vLLM
Triton Inference Server
Ray Serve
SGLang
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 Ob
📌 Sr. MLOps Engineer (Bengaluru)
🏢 UST
📍 Bengaluru