MLOps Engineer | Bangalore

Overview
We are seeking a Mid-Level ML Dev / Cloud Engineer to support the development deployment and optimization of machine learning services in a cloud-native environment. This role focuses on building scalable pipelines integrating models into production and ensuring reliable cloud infrastructure for ML applications. The ideal candidate has hands-on experience with ML workflow tools cloud orchestration and software development best practices.

Responsibilities

• Develop maintain and optimize ML pipelines including data ingestion preprocessing feature engineering and model deployment.

• Integrate machine learning models into production-grade APIs and services.

• Collaborate with data scientists to transition research models into scalable cloud-ready solutions.

• Build automated workflows for model training evaluation monitoring and CI/CD.

• Manage and optimize cloud infrastructure for compute storage orchestration and networking.

• Implement model performance monitoring logging and automated alerting.

• Ensure reliability scalability and cost-efficiency of ML environments.

• Support containerization and microservices deployment using Docker/Kubernetes.

• Troubleshoot production ML workflows and resolve performance bottlenecks.

• Follow best practices for security compliance and version control within ML and

Requirements

• 35 years of hands-on experience in machine learning engineering MLOps or cloud engineering.

• Strong foundations in Python ML workflows and API development.

• Experience deploying models into production using Docker/Kubernetes.

• Practical experience with at least one major cloud provider (AWS GCP or Azure).

• Familiarity with ML lifecycle tools (MLflow Airflow Kubeflow or similar).

• Experience building or maintaining CI/CD pipelines.

• Understanding of distributed systems container orchestration and cloud-native architectures.

• Ability to collaborate with data scientists engineers and stakeholders.

• Excellent problem-solving skills and comfort working in a fast-paced environment.

Tech Stack

Cloud Services (one or more):

• AWS: S3 SageMaker Lambda EC2 EKS

• GCP: GCS Vertex AI Cloud Run GKE

• Azure: Blob Storage ML Studio AKS

ML / MLOps Tools:

• MLflow Kubeflow Airflow TFX SageMaker Pipelines

• Model serving frameworks: TensorFlow Serving TorchServe FastAPI

Languages & Frameworks:

• Python (NumPy Pandas Scikit-learn PyTorch TensorFlow)

• Bash SQL

• API development (FastAPI Flask Django)

DevOps & Infra:

• Docker Kubernetes

• CI/CD tools (GitHub Actions GitLab CI Jenkins)

• Terraform or CloudFormation for IaC

• Monitoring: Prometheus Grafana CloudWatch