Data Scientist (MLOps) - REMOTE

About the job Data Scientist (MLOps) - REMOTE

Work Experience : 3+ years

Industry : IT Services

Remote Job

Job Description

Data Scientist (MLOps)

Job Description:

We are seeking a versatile and adaptable Data Scientist with expertise in a range of

technology domains, including Network Operations, Infrastructure Management,

Cloud Computing, MLOps, Deep Learning, NLP, DevOps, LLM infrastructure.

This role encompasses a wide range of responsibilities, including designing and

implementing cloud solutions,

building MLOps pipelines on cloud platforms (AWS, Azure), orchestrating CI/CD

pipelines using tools like GitLab CI and GitHub Actions,

and taking ownership of data pipeline and engineering infrastructure design to

support enterprise machine learning systems at scale.

Responsibilities:

Infra:

Manage cloud-based infrastructure on AWS and Azure, focusing on scalability and

efficiency.

Utilize containerization technologies like Docker and Kubernetes for application

deployment.

NetOps

Monitor and maintain network infrastructure, ensuring optimal performance and

security.

Implement load-balancing solutions for efficient traffic distribution.

Infrastructure and Systems Management:

Cloud Computing:

Design and implement cloud solutions, including the development of MLOps

pipelines.

Ensure proper provisioning, resource management, and cost optimization in a cloud

environment.

MLOps and DevOps:

Orchestrate CI/CD pipelines using GitLab CI and GitHub Actions for streamlined

software delivery.

Collaborate with data scientists and engineers to operationalize and optimize data

science models.

Apply software engineering rigor, including CI/CD and automation, to machine

learning projects.

Data Pipelines and Engineering Infrastructure:

Design and develop data pipelines and engineering infrastructure to support

enterprise machine learning systems.

Transform offline models created by data scientists into production-ready systems.

Build scalable tools and services for machine learning training and inference.

Technology Evaluation and Integration:

Identify and evaluate new technologies to enhance the performance, maintainability,

and reliability of machine learning systems.

Develop custom integrations between cloud-based systems using APIs.

Proof-of-Concept Development:

Facilitate the development and deployment of proof-of-concept machine learning

systems.

Emphasize auditability, versioning, and data security during development.

Requirements:

Strong software engineering skills in complex, multi-language systems.

Proficiency in Python and comfort with Linux administration.

Experience working with cloud computing and database systems.

Expertise in building custom integrations between cloud-based systems using APIs.

Experience with containerization (Docker) and Kubernetes in cloud computing

environments.

Familiarity with data-oriented workflow orchestration frameworks (KubeFlow,

Airflow, Argo, etc.).

Ability to translate business needs into technical requirements.

Strong understanding of software testing, benchmarking, and continuous integration.

Exposure to machine learning methodology and best practices.

Exposure to deep learning approaches and modeling frameworks (PyTorch,

TensorFlow, Keras, etc.).

If you are a dynamic engineer with a diverse skill set, from cloud computing to

MLOps and beyond, and you are eager to contribute to innovative projects in a

collaborative environment, we encourage you to apply for this challenging and

multifaceted role. Join our team and help us drive technological excellence across
 our organization.,