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MLOps Engineer

Fx31 Labs (Formerly Fractal31)

3 years

0 Lacs

Greater Kolkata Area

Posted:1 week ago| Platform:

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Skills Required

drive deployment scaling maintenance learning data ai design ml model training automate monitoring logging tracking versioning governance docker orchestration software scalability security documentation devops aws azure gcp sagemaker vertex python tensorflow pytorch containerization github jenkins gitlab pipeline airflow drift code terraform spark compliance cutting collaborative development compensation

Work Mode

Remote

Job Type

Full Time

Job Description

Job Title :

MLOps Engineer

Location :

Remote

Experience Required :

3+ years

Employment Type :

Full-Time

About The Role

We are seeking an experienced MLOps Engineer to join our team and drive the deployment, scaling, and maintenance of machine learning systems in production. In this role, you will bridge the gap between data science and operations to ensure our AI solutions are robust, reliable, and scalable.

Key Responsibilities

Design, build, and maintain end-to-end ML pipelines including data ingestion, model training, validation, and deployment.
Develop and manage CI/CD workflows for machine learning models.
Automate model monitoring, logging, and performance tracking in production environments.
Implement model versioning, reproducibility, and governance best practices.
Manage containerized deployments using Docker and orchestration platforms like Kubernetes.
Work closely with data scientists and software engineers to productionize ML solutions.
Optimize infrastructure costs and ensure scalability, security, and reliability.
Create and maintain documentation, guidelines, and technical standards.

Key Skills & Qualifications

3+ years of hands-on experience in MLOps, DevOps, or related roles in production environments.
Strong experience with cloud platforms (AWS, Azure, or GCP) and their ML services (SageMaker, Vertex AI, Azure ML).
Proficiency with Python and familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn).
Solid understanding of containerization and orchestration tools (Docker, Kubernetes).
Experience with CI/CD tools (GitHub Actions, Jenkins, GitLab CI) applied to ML workflows.
Knowledge of data pipeline frameworks (Airflow, Prefect, Kubeflow).
Familiarity with monitoring tools and model drift detection.
Strong problem-solving skills and the ability to work independently in a remote environment.

Nice To Have

Experience with infrastructure-as-code (Terraform, CloudFormation).
Understanding of feature stores and model registries.
Exposure to big data technologies (Spark, Databricks).
Knowledge of security and compliance in ML deployments.

Benefits

100% Remote work flexibility.
Work on cutting-edge AI solutions with a collaborative team.
Learning and development support.
Competitive compensation and benefits.

(ref:hirist.tech)

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