Senior MLOps Engineer

Tranzeal; Bangalore Rural, Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Tranzeal seeks a hands-on Senior MLOps Engineer to lead end-to-end lifecycle management of DL models, LLMs, and SLMs. The role involves deployment across cloud, on-premises, hybrid, and air-gapped environments with emphasis on governance, observability, and scalable inference platforms.

You will collaborate with Data Scientists, ML Engineers, DevOps, Platform Engineering, and Security teams to optimize latency, throughput, and GPU utilization while maintaining robust model governance and

Qualifications

• 35 years of hands-on experience in MLOps, LLMOps, ML Engineering, or AI Engineering.
• Strong Python development skills.
• Hands-on experience with Databricks and/or Azure ML.
• Strong understanding of Deep Learning, LLMs, SLMs, RAG, and Hugging Face.
• Experience deploying models using PyTorch and TensorFlow.

Responsibilities

• Design, build, and manage MLOps and LLMOps pipelines.
• Deploy, host, scale, and optimize Deep Learning models, LLMs, and SLMs.
• Manage the complete model lifecycle including versioning, deployment, rollout, rollback, monitoring, and retirement.
• Deploy and manage models on Kubernetes, OpenShift, Databricks, and GPU-based infrastructure.
• Implement Model Governance, Model Registry, lineage, approval workflows, and compliance controls.
• Build and maintain model monitoring, observability, tracing, logging, and drift detection.
• Optimize model latency, throughput, GPU utilization, inference performance, and infrastructure cost.
• Work across Cloud, On-Premises, Hybrid, and Air-Gapped environments.
• Implement secure and scalable AI/ML inference platforms and deployment architectures.

Skills

Python

DL/ML

LLMs

ML Engineering

Model Deployment

Tools

Kubernetes

OpenShift

Databricks

Azure ML

GPU Infrastructure

Job description

Senior MLOps Engineer

Experience: 5 Years
Location: ITPL, Bangalore
Work Mode: Work from Office 5 Days a Week

Role: Senior MLOps Engineer
Industry: AI / Machine Learning / Generative AI

Job Summary

We are looking for a hands-on Senior MLOps Engineer / AI Deployment Engineer with strong expertise in MLOps, LLMOps, ML Engineering, Model Deployment, Model Governance, and Model Observability.

The role involves owning the end-to-end lifecycle of Deep Learning models, LLMs, and SLMs, including deployment, scaling, monitoring, optimization, governance, and retirement across cloud, on-premises, hybrid, and air-gapped environments.

Key Responsibilities

• Design, build, and manage MLOps and LLMOps pipelines.
• Deploy, host, scale, and optimize Deep Learning models, LLMs, and SLMs.
• Manage the complete model lifecycle including versioning, deployment, rollout, rollback, monitoring, and retirement.
• Deploy and manage models on Kubernetes, OpenShift, Databricks, and GPU-based infrastructure.
• Implement Model Governance, Model Registry, lineage, approval workflows, and compliance controls.
• Build and maintain model monitoring, observability, tracing, logging, and drift detection solutions.
• Optimize model latency, throughput, GPU utilization, inference performance, and infrastructure cost.
• Work across Cloud, On-Premises, Hybrid, and Air-Gapped environments.
• Implement secure and scalable AI/ML inference platforms and deployment architectures.
• Collaborate with Data Scientists, ML Engineers, DevOps, Platform Engineering, and Security teams.

Must-Have Skills

• 35 years of hands-on experience in MLOps, LLMOps, ML Engineering, or AI Engineering.
• Strong Python development skills.
• Hands-on experience with Databricks and/or Azure ML.
• Strong understanding of Deep Learning, LLMs, SLMs, RAG, and Hugging Face.
• Experience deploying models developed using PyTorch and TensorFlow.
• Strong hands-on experience with model deployment on:
  • Kubernetes
  • Databricks
  • GPU Infrastructure
  • OpenShift

LLM / Model Serving

Experience with one or more of the following:

• vLLM
• Triton Inference Server
• Ray Serve
• SGLang
• Databricks Model Serving

GPU & Inference Optimization

• Strong knowledge of NVIDIA GPUs and CUDA.
• Experience with multi-GPU deployments.
• Hands-on experience with LLM inference optimization, GPU utilization, latency, and throughput optimization.

Model Governance & Observability

• Model Registry and Model Lifecycle Management.
• Model Governance and Model Lineage.
• Model Monitoring and Drift Detection.
• AI/ML Observability and Distributed Tracing.
• Experience with:
  • MLflow
  • OpenTelemetry
  • Langfuse
  • Splunk
  • Grafana / ELK

Databases & Vector Databases

Strong database knowledge in one or more of:

• SQL Server
• PostgreSQL
• Oracle
• MySQL
• MongoDB

Experience with Vector Databases / Search platforms such as:

• Pinecone
• Chroma
• FAISS
• Milvus
• Azure AI Search

API & Integration

• REST APIs
• WebSockets
• Streaming HTTP
• Experience building scalable AI/ML inference APIs.

DevOps / CI-CD

• Jenkins
• Azure DevOps
• CI/CD automation for ML and AI workloads.
• Containerization and orchestration using Docker and Kubernetes.

Infrastructure & Security

• Experience working across Cloud, On-Premises, Hybrid, and Air-Gapped environments.
• Experience with authentication and authorization solutions such as Keycloak.
• Understanding of Model Security, Governance, and Compliance.

Good-to-Have Skills

• Kafka, RabbitMQ, Azure Event Hub.
• Fine-tuning and model optimization.
• LLM quantization and inference optimization.
• Model Governance and AI Security.
• Experience with open-source and enterprise LLMs such as:
  • Llama
  • Mistral
  • DeepSeek
  • Qwen
  • Phi
  • Gemma

Warm regards,

Laya Guptha|Senior IT Recruiter

Email: [e-mail]