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Weekday AI (YC W21)
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MLops Engineer

MLops Engineer

Weekday AI (YC W21)

5 years

0 Lacs

hyderabad telangana india

Posted:3 weeks ago| Platform:

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Skills Required

cutting ai automation learning devops ml design data integration aws gcp azure kubernetes monitoring model drift automate terraform helm compliance scalability coding python jenkins github gitlab tensorflow pytorch docker airflow mlflow sagemaker vertex versioning scaling

Work Mode

On-site

Job Type

Full Time

Job Description

This role is for one of Weekday's clientsMin Experience: 5 yearsLocation: HyderabadJobType: full-time

Requirements

Techsophy

, we are driving transformation for global enterprises with cutting-edge AI and automation. We are seeking an

MLOps Engineer (with 5+ years of experience)

who can bridge the gap between

Machine Learning and DevOps,

building scalable ML pipelines and ensuring models thrive in real-world production environments.

What You'll Do

Design, build, and optimize end-to-end ML pipelines from data ingestion to deployment
Collaborate with Data Scientists, Data Engineers, and Cloud Architects to bring AI models to life
Implement CI/CD for ML workflows, ensuring seamless integration and faster releases
Deploy ML models in cloud & containerized environments (AWS/GCP/Azure, Kubernetes, Docker)
Set up monitoring systems for model performance, drift detection, and observability
Automate infrastructure with Terraform, Helm, and Kubernetes operators
Ensure compliance, scalability, and cost-optimized ML solutions for our enterprise client.

What You Bring

5+ years of experience in MLOps / DevOps for ML systems
Strong coding skills in Python, Bash, and CI/CD tools (Jenkins, GitHub Actions, GitLab CI)
Hands-on expertise with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
Deep knowledge of Docker, Kubernetes, Airflow/Prefect, and MLflow/Weights & Biases
Experience with cloud ML services: AWS SageMaker, Azure ML, or GCP Vertex AI
Solid understanding of data versioning (DVC, LakeFS) and feature stores (Feast)
Passion for automation, scaling ML systems, and solving real-world AI challenges

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