- ML Pipeline Development: Collaborate with data scientists and engineers to design, build, and optimize ML pipelines. Leverage cloud frameworks (preferably GCP) to create efficient and scalable pipelines.
- Technical Problem Solving: Investigate and resolve technical issues related to ML pipelines. Provide level 2 technical support and perform root cause analysis for production errors.
- Collaboration and Code Standardization: Work closely with cross-functional teams, including developers, testers, and system administrators. Implement tools for collaboration, code standardization, and repeatability (e.g., Git/GitHub).
- Data Quality and Complexity: Appreciate the nuances of data quality and complexity. Understand how data can be leveraged to solve business problems effectively.
- Software Engineering and Health Care Data: Prior experience in software engineering and/or working with health care data is a plus.
- Supporting Gen AI Projects: Contribute to the development and deployment of AI models, particularly those related to Generative AI (Gen AI). Collaborate with data scientists and researchers to ensure smooth integration of Gen AI solutions into our existing infrastructure.
- Education: Bachelor's degree in Computer Science, Software Engineering, Data Science, Mathematics, or a related field. A Master's degree is advantageous.
- ML and NLP Knowledge: Solid understanding of practical and theoretical aspects of standard ML and NLP concepts. Ability to participate in end-to-end pipelines for model development and deployment.
- Experience in the Healthcare or related industry.
- Cloud Proficiency: Experience with cloud computing environments (GCP or AWS) and cloud services.
- Programming Skills: Advanced proficiency in Python, Java, and R.
- ML Frameworks: Extensive knowledge of ML frameworks such as TensorFlow, Keras, and PyTorch.
- Data Processing: Proficiency in leveraging SQL, MapReduce/Hive/Spark, and modern data processing frameworks in a production setting.
- DevOps and CI/CD: Experience in DevOps practices using test automation, CI/CD tools, and cloud infrastructure.
- Containerization: Familiarity with Docker/containerized workflows for model management.
- Data Warehousing: Exposure to data warehousing tools/environments (e.g., Snowflake, Teradata, SQL Server, Google Big Query).
- Infrastructure as Code: Knowledge of IaaS (e.g., Terraform) for implementing scalable cloud/on-prem environments.
- CI/CD Processes: Proficiency in developing and maintaining CI/CD processes for enterprise SaaS and on-prem applications (using tools like GitHub, Jenkins, Maven, Gradle, GitLab, etc.).
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DevOps Engineer - Philadelphia, United States - Brooksource
Description
DevOps Engineer
Location: Philadelphia, Pennsylvania
A healthcare client of ours is seeking a talented and experienced DevOps Engineer to join their dynamic Data Science team. As a DevOps Engineer, you will play a crucial role in building and maintaining robust ML pipelines, ensuring seamless deployment of models into production environments and supporting Gen AI projects. Your expertise will contribute to the success of our data-driven initiatives.
Responsibilities:
Qualifications:
Eight Eleven Group provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, national origin, age, sex, citizenship, disability, genetic information, sexual orientation, gender identity, marital status, amnesty or status as a covered veteran in accordance with applicable federal, state, and local laws.
