MLOps Engineer

Aspyra Hr Services; Dadri, Gurugram District, Delhi; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Aspyra Hr Services in Gurugram is seeking an experienced MLOps Engineer to build and manage end-to-end ML operations, data workflows, and automated deployment pipelines. The role requires strong Python, PySpark and CI/CD skills, plus hands-on Docker and Kubernetes experience.

You will collaborate with data science and DevOps teams to productionize ML models. Ideal candidates have 4+ years in MLOps/ML Engineering and cloud exposure across AWS, Azure, or GCP, contributing to scalable ML

Qualifications

• 4+ years in MLOps/ML Engineering/Data Engineering.
• Strong hands-on with Python, PySpark, CI/CD and Git.
• Experience Docker/containerization and Kubernetes/orchestration.
• Experience on AWS, Azure or GCP.
• Understanding of ML lifecycle, automation and production deployment.

Responsibilities

• Build and manage end-to-end MLOps pipelines for ML development and deployment.
• Develop data/ML workflows using Python and PySpark.
• Implement CI/CD pipelines for automated testing, deployment and monitoring.
• Containerize ML apps with Docker and manage orchestration with Kubernetes.
• Work with cloud services (AWS/Azure/GCP) for scalable ML infrastructure.
• Implement model deployment, versioning, monitoring and automation.
• Collaborate with Data Scientists, Data Engineers and DevOps to productionize models.

Skills

MLOps

Python

PySpark

CI/CD

Git

Containerization

Orchestration

Cloud (AWS/Azure/GCP)

Tools

Docker

Kubernetes

Job description

MLOps Engineer

Location: Gurugram

Experience: 4+ Years

Type: Permanent Full-Time

Mandatory Skills: MLOps, Python, PySpark, CI/CD, Git, Containerization, Orchestration, Cloud (AWS/Azure/GCP)

Role & Responsibilities

• Build and manage end-to-end MLOps pipelines for ML model development and deployment.
• Develop data/ML workflows using Python and PySpark.
• Implement CI/CD pipelines for automated model testing, deployment and monitoring.
• Containerize ML applications using Docker and manage orchestration using Kubernetes or equivalent platforms.
• Work with AWS/Azure/GCP cloud services for scalable ML infrastructure.
• Implement model deployment, versioning, monitoring and automation.
• Collaborate with Data Scientists, Data Engineers and DevOps teams to productionize ML models.

Preferred Candidate Profile

• 4+ years of experience in MLOps / ML Engineering / Data Engineering.
• Strong hands-on experience with Python, PySpark, CI/CD and Git.
• Practical experience in Docker/containerization and Kubernetes/orchestration.
• Experience working on at least one major cloud platform: AWS, Azure or GCP.
• Strong understanding of ML lifecycle, automation and production deployment.