Machine Learning Engineer (15265-1) Newark, NJ

ESRhealthcare•Newark, NJ

14h

About The Position

As a Machine Learning Engineer, you will play a pivotal role in driving the development and implementation of cutting-edge machine learning solutions for our client. Your responsibilities will encompass a wide range of tasks, from leading a small team of machine learning engineers to collaborating with cross-functional teams to deliver impactful solutions. You will be at the forefront of driving innovation and leveraging the power of machine learning to solve real-world problems, drive business growth, and create value.

Requirements

7+ years of experience in Machine Learning.
Extensive software engineering experience with strong working experience as a Machine Learning Engineer.
Bachelor's degree in computer science, computer engineering, or a related engineering field.
Advanced proficiency with Python, Java, and Scala.
Strong computer science fundamentals such as algorithms, data structures, multithreading.
Experience working with Generative AI, using LangChain for Gen AI and techniques like RAG.
Experience using ML and DL Libraries:XGBoost, SKlearn, Tensorflow or PyTorch
In-depth experience building solutions using public clouds such as AWS, GCP.
Experience using ML platforms like SageMaker, H2O, DataRobot, etc.
Strong knowledge on ML model development life cycle components like containers, batch vs real time inference endpoints, application security testing etc.
Experience managing relationships in a cross-functional environment with multiple stakeholders.
Experience with developing and deploying production-grade applications with ML inferences using automation pipeline on cloud.
Experience working in Agile/ Scrum development process.
Thought leadership and innovative thinking.
Excellent communication and collaboration skills.

Nice To Haves

Masters degree preferred.

Responsibilities

Lead and drive machine learning projects from inception to production: build relationships with business partners and cross-functional teams.
Collaborate with business leaders, subject matter experts, and decision-makers to develop success criteria and optimize new products, features, policies, and models.
Partner with data scientists to understand, implement, train, and design machine learning models.
Collaborate with the infrastructure team to improve the architecture, scalability, stability, and performance of ML platform.
Construct optimized data pipelines to feed machine learning models.
Extend existing machine learning libraries and frameworks.
Develop processes, model monitoring, and governance framework for successful ML model operationalization.
Define objectives for the Machine Learning platform, own the technical roadmap, and be accountable for delivering results.
Define standards for engineering and operational excellence for running best-in-class ML platforms and continue to improve ML platforms to keep up with the latest innovations.
Design and implement the best architectural practices in the delivery of data science use cases.