Cloud engineer Kubernetes MLOPS and fast Api
- Harshvani Global
- 2 to 6 Yrs
- haryana
- 1 month ago
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Job Description
- Job Title: Cloud Engineer Data Science (MLOps & ML Engineering) Location: Gurugram (On-site) Experience: 36 years Employment Type: Full-Time About the Role: We are seeking a Cloud Engineer with a strong background in Data Science, Machine Learning, Python Development, MLOps practices, and Cloud Kubernetes Deployments. The ideal candidate must have hands-on expertise with FastAPI, MLflow, Kubeflow, and should be proficient in building, deploying, and managing ML pipelines in production. This is an on-site role in Gurugram. Candidates must be willing to work from the office and align with our agile project timelines. Key Responsibilities: Design, build, and optimize end-to-end MLOps pipelines using MLflow, Kubeflow, and other industry best practices. Develop scalable, production-grade APIs using FastAPI for ML model serving and orchestration. Manage Kubernetes clusters for scalable ML workflows; automate deployment of ML models on Kubernetes environments. Implement continuous integration and continuous deployment (CI/CD) pipelines specifically tailored for ML operations. Collaborate with data scientists and machine learning engineers to operationalize ML models. Optimize cloud infrastructure for machine learning workloads (AWS, Azure, or GCP experience preferred). Monitor, maintain, and troubleshoot deployed ML services and models to ensure maximum uptime and performance. Ensure model versioning, reproducibility, and tracking through MLflow. Work with Kubeflow Pipelines for orchestrating complex ML workflows. Implement model monitoring, drift detection, and automated retraining strategies. Required Skills and Qualifications: Bachelor's or Masters degree in Computer Science, Data Science, Engineering, or a related field. Strong Python programming skills with exposure to ML and API development. 2+ years experience working with FastAPI for building APIs. Hands-on experience with MLflow for model tracking and management. Hands-on experience with Kubeflow for deploying ML pipelines on Kubernetes. Deep understanding of Docker and Kubernetes (K8s) deployments. Good knowledge of MLOps practices versioning, monitoring, retraining, and scaling ML models. Experience with at least one major cloud platform (AWS, Azure, GCP). Strong understanding of ML lifecycle management and production challenges. Knowledge of CI/CD pipelines (Jenkins/GitLab CI preferred). Solid foundation in machine learning and data science concepts. Preferred Skills (Good to Have): Exposure to cloud-native services like AWS SageMaker, Azure ML, or GCP AI Platform. Experience with distributed training and inference systems. Understanding of serverless architectures. Familiarity with Prometheus/Grafana for model and infrastructure monitoring. Soft Skills: Strong analytical and problem-solving skills. Ability to work collaboratively in a cross-functional team environment. Excellent communication and documentation skills. Proactive attitude and willingness to take ownership of projects. Job Types: Full-time, Permanent Work Location: In person Speak with the employer +91 hidden_mobile,
Employement Category:
Employement Type: Full timeIndustry: IT Services & Consulting
Role Category: Not Specified
Functional Area: Not Specified
Role/Responsibilies: Cloud engineer Kubernetes MLOPS and fast Api
Contact Details:
Company: Dataviv Media NetworkLocation(s): Haryana
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Keyskills: Data Science Machine Learning AWS Azure GCP Docker Jenkins
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