About the Role:

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

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 professional with strong cloud, Kubernetes, CI/CD and ML infrastructure expertise, capable of building reliable and scalable production ML platforms.