We're looking for an experienced MLOps Engineer to drive ML automation, deployment and platform reliability.

Senior MLOps Engineer (DevOps) | 6-9 Y | Bangalore (Hybrid) 3 Days Office | Quick Joiner |

Key Technical Skills:

DevOps & Cloud Infrastructure

Python, Bash / Go

Docker and Kubernetes

AWS / Azure / GCP

Terraform, Pulumi or CloudFormation

CI/CD & ML Pipelines

GitHub Actions, GitLab CI, Jenkins or Argo Workflows

Kubeflow, Airflow, Prefect or Metaflow

Automated model testing, versioning and deployment

Canary deployments and ML workflow automation

Model Deployment & Serving

TorchServe, NVIDIA Triton, TensorFlow Serving

Seldon, BentoML or similar

Batch and real-time model serving

Monitoring & MLOps

Prometheus, Grafana, Datadog

MLflow, Weights & Biases or Neptune

Model monitoring, data drift and performance tracking

Feature stores – Feast / Tecton

Data versioning – DVC / LakeFS

Key Responsibilities:

Build and manage end-to-end ML pipelines.

Automate model training,



validation and deployment.

Develop robust CI/CD pipelines for ML workflows.

Manage scalable Kubernetes-based ML infrastructure and GPU clusters.

Implement model monitoring, observability and alerting.

Maintain experiment tracking, model registries and data lineage.

Optimize cloud infrastructure performance and costs.

Collaborate with Data Science teams to accelerate production AI/ML deployments.

Good to Have:

Experience with GPU infrastructure and distributed ML workloads.

Exposure to LLM deployment, Vector Databases and RAG infrastructure.

Experience with feature stores and data governance.

Ideal Candidate: A hands-on MLOps / DevOps qualified with strong cloud, Kubernetes, CI/CD and ML infrastructure expertise, capable of building reliable and scalable production ML platforms.

📌 Senior MLOps Engineer (DevOps) | 6-9 Y | Bangalore (Hybrid) 3 Days Office | Quick Joiner |
🏢 Accedepro Private
📍 Bengaluru