ML Cloud 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 

• 3–5 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