Job description

MLOps Engineer

Remote

Fulltime

Requirements

What We Need:

Bachelor’s in Computer Science, Statistics, Math, or Data Science or 4 additional years of experience.

A minimum of four (4) years of experience in practicing AI/ML and Data Science.

Shall possess strong architecture & design experience, including at least three (3) years of experience deploying production enterprise applications in AWS that use AI/ML.

Proven experience in developing and deploying AI/ML models.

Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS).

Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc.

Experience with Docker and Kubernetes, OpenShift.

Excellent knowledge of cloud computing AWS and Sagemaker, Sagemaker Pipeline and Model Registry.

Understanding the addressing and metadata standards and experience in handling large data sets and relational databases.

Ability to understand tools used by data scientists and experience with software development and test automation.

Experience with building data pipelines, model framework and model deployment.

Hands-on experience in building machine learning and statistical models using Python, PySpark, Scala, Machine Learning libraries, SQL.

Experience in data management and data analysis in a relational database, Hadoop and Spark.

Ability to conduct research work collaboratively as well as independently.

Shall possess strong hands-on experience with Docker, Jenkins, Terraform, and API platforms such as Apigee.

Shall have experience in large-scale, high-performance enterprise big data application deployment and solution architecture on complex heterogeneous environments in AWS.

Experience developing with containers and Kubernetes in cloud computing environments.

Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)

Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent).

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

Able to present articulately at client meetings.

Great project and client management skills.

Excellent problem-solving skills and attention to detail and ability to identify and measure KPIs.

Ability to work independently and in a team environment and have critical thinking capabilities.

Commanding skills with Tensorflow, Pytorch and other deep learning framework.

Technical Skills:

Extremely strong hands-on EKS, IAM, RDS, S3, Glue interactive Kernel, Sagemaker Studio.

Expert knowledge in AWS, Sagemaker Pipeline, Sagemaker Model registry, AWS code pipeline, Sagemaker end point and Trigger event.

Experience in report designing & visualization tools such as AWS Quick Sight, Tableau, etc.

Advanced AI/ML tools, including low code, no code, RPA tools.

Analytics Skills:

Ability to analyze and report on complex data.

Should be comfortable with numerical and quantitative calculations and analysis.

Strong problem-solving, multitasking, organization, and time management skills

Job Type: Full-time

Pay: $100,000.00 - $140,000.00 per year

Benefits:

• 401(k)
• Health insurance
• Paid time off

Schedule:

• 8 hour shift

Experience:

• build MLOps pipelines on cloud solutions (AWS).: 2 years (Preferred)
• AI/ML and Data Science: 5 years (Preferred)
• Deploying production enterprise app in AWS that use AI/ML: 3 years (Preferred)
• MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow: 2 years (Preferred)

Work Location: Remote

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