ML Engineer with Strong MLOps, LLMOps Experience || Hybrid 3x/week onsite in Malvern, PA

DMS Vision•Frisco, AL

2d•Hybrid

About The Position

This role focuses on owning the end-to-end Machine Learning Pipeline, including CI/CD for ML Engineering and Productionization. The primary goal is to manage code, versioning of datasets, models, and production endpoints to facilitate collaboration, experimentation, and rapid scaling for ML Engineers. The position requires developing end-to-end (Data/Dev/ML)Ops pipelines based on a deep understanding of cloud platforms, the AI lifecycle, and business challenges to ensure efficient, predictable, and sustainable delivery of analytics solutions. Key activities include implementing model monitoring, productionizing GenAI applications, and leveraging expertise in cloud architecture/DevOps to operationalize AI/ML analytics. The role involves building and automating the AI/ML workstream from data analysis and experimentation to operationalization, model training, tuning, and visualization, while also improving and maintaining the automated CI/CD pipeline. Collaboration with data scientists on model evaluation and training, and with AI/ML practitioners to solve complex problems and create MLOps solutions, is essential. Continuous evaluation of the latest ML ecosystem packages and frameworks is also a part of the role.

Requirements

Experience in configuring AWS environment (i.e. EC2, S3, DB's etc.)
Data(Glue, EMR,etc.) and AWS Services (Step & Lambda Functions etc.)
Familiarity with container technologies including AWS Fargate, Docker and Kubernetes
Proficient in AWS, DevOps, CI/CD and Microservices
Expert in Container technologies like Kubernetes and Docker
Experience developing and managing packages and APIs using Python
Expertise in developing and deploying ML models in AWS Sagemaker
Continuous Integration for Machine Learning projects.
Continuous Delivery for Machine Learning projects.

Nice To Haves

Strong MLOps, LLMOps Experience

Responsibilities

Own the end-to-end Machine Learning Pipeline together with CI/CD for our ML Engineering and Productionization process.
Focus on code, versioning of datasets, models and production endpoints to allow ML Engineers to collaborate, experiment and scale fast.
Develop end-to-end (Data/Dev/ML)Ops pipelines based on in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably.
Implement model monitoring
Productionize GenAI Applications
Analyze and recommend enterprise-grade solutions for operationalizing AI / ML analytics.
Build and automate our AI/ML workstream from data analysis, experimentation, operationalization, model training, model tuning to visualization.
Improve and maintain our automated CI/CD pipeline.
Assist data scientists with model evaluation and training (includes versioning, compliance and validation).
Build and maintain data pipelines for analytics, model evaluation and training (includes versioning, compliance and validation).
Work with AI/ML practitioners to solve complex problems and create unique solutions for MLOps.
Continuously evaluate the latest packages and frameworks in the ML ecosystem.
Improve and advance DataOps and MLOps infrastructure and operational processes.