Machine Learning Engineer

Philadelphia, PA

Hybrid(3 days onsite)

Contract to hire

The largest health insurer in Philadelphia is seeking a machine learning (ML) operations Engineer to collaborate with our Data Scientists to refine and optimize our ML production pipelines to deliver high-quality, high-value solutions to business stakeholders. The ML Operations Engineer will evaluate and enhance our existing development and deployment processes with near real-time data and automate model performance tracking.

Responsibilities:

Work with Data Scientists to seamlessly connect the gap from development to production for our ML and NLP models under the constraints of scalability, correctness, maintainability, and efficiency
Develop scripts and processes to optimize and automate ML and NLP models in production within Google Cloud and bring subject matter expertise about data storage and object-oriented programming
Ensure appropriate hardware resources are selected to run models with the required latencies relative to computational cost constraints
Develop strategies to track model performance and drift, identifying models that need to be retrained
Document and work to standardize repeatable machine learning processes for operationalizing and automating models quickly, reliably, and reproducibly
Collaborate with Data Scientists to extend existing ML and NLP libraries and frameworks based on team needs
Contribute to overall development of data and CI/CD pipelines in collaboration with Data Science, Data Engineering, and DevOps teams
Write unit tests and maintain high code quality, per both static code analysis & team standards

Technical skills:

Solid understanding of practical and theoretical aspects of standard ML and NLP concepts to enable participation in the full end-to-end pipelines for developing and deploying models effectively into production
Experience with cloud computing environments including GCP or AWS and cloud services
Proficiency in Python, Java, and R
Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
Proficiency with ML framework such as TensorFlow, Keras, and PyTorch
Experience in DevOps using test automation and CI/CD tools, Docker, cloud infrastructure, and other modern technologies, CI/CD pipeline set up from scratch as DevOps
Experience with IaaS (Terraform) implementing scalable cloud / On-prem based environments and systems; service-oriented architectures and microservices; deploying resilient, scalable, high-throughput systems.
Experience in developing and maintaining CI/CD processes for enterprise SaaS and on-prem applications using tools like GitHub, Jenkins, Maven, Gradle, GitLabs etc.

Qualifications:

2-3 year experience building ML pipelines in a cloud framework, GCP preferred
BSc in Computer Science, Software engineering, Data Science, Mathematics, or related field; Masters degree is a plus
Experience working with tools for collaboration, code standardization, and repeatability, and versioning (Git/Git Hub)
Appreciation for data quality, complexity, and understanding when and how data can be used to understand business problems and questions
Software engineer experience a plus
Experience with health care data a plus

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