MLOps Engineer

Tranzeal

Bengaluru

On-site

INR 3,000,000 - 5,500,000

Full time

14 days+

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Job summary

Tranzeal is seeking a hands-on Senior MLOps Engineer to own the end-to-end deployment, governance and observability of DL models, LLMs and SLMs. The role covers cloud, on-prem, hybrid and air-gapped environments, with a focus on robust model lifecycle management.

You will leverage Python, Databricks or Azure ML, and deploy models using PyTorch/TensorFlow across Kubernetes and GPU infrastructure, ensuring scalable, reliable AI operations.

Qualifications

  • Hands-on ML engineering with model deployment, governance and observability.
  • Own full lifecycle of DL models, LLMs and SLMs across cloud, on-prem, hybrid and air-gapped environments.
  • Strong Python development skills.
  • Experience with Databricks and/or Azure ML.
  • Experience deploying models built with PyTorch and TensorFlow.

Responsibilities

  • Own complete lifecycle of DL models, LLMs and SLMs.
  • Deploy and monitor model performance across environments.
  • Collaborate on governance and observability practices.
  • Optimize Kubernetes deployments and GPU infrastructure.

Skills

Python development
Deep Learning
LLMs
RAG
Hugging Face
TensorFlow
PyTorch

Tools

Databricks
Azure ML
PyTorch
TensorFlow
Kubernetes
GPU Infrastructure

Job description

Senior MLOps Engineer

Experience: 5 Years

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

35 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:

  • Kubernetes
  • Databricks
  • GPU Infrastructure
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