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Machine Learning Engineer (Hybrid Schedule)

Job Title
Machine Learning Engineer (Hybrid Schedule)
Job ID
27706119
Work Hybrid
Yes
Location
St. Louis,  MO 63134 Hybrid
Other Location
Description
Our History: 
From our start in 2009, Conexess has established itself in 3 markets, employing nearly 200+ individuals nation-wide. Operating in over 15 states, our client base ranges from Fortune 500/1000 companies to mid-small range companies. For the majority of the mid-small range companies, we are exclusively used due to our outstanding staffing track record.

Who We Are:
Conexess is a full-service staffing firm offering contract, contract-to hire, and direct placements. We have a wide range of recruiting capabilities extending from help desk technicians to CIOs. We are also capable of offering project-based work.

Conexess Group is aiding a large healthcare client in their search for a Machine Learning Engineer. This is a long-term opportunity with a competitive compensation package.

This position requires a candidate local to the following locations:
  • Austin TX
  • St. Louis, MO
  • Morris Plains, NJ
******We are unable to work C2C on this role******

Responsibilities:
  • Machine learning libraries: TensorFlow, PyTorch, Scikit-learn, Deploying and optimizing different pipelines that support various data science processes
  • Establish and set up model life cycle management with tools like MLFlow, etc
  • Developing and deploying Spark/Databricks jobs with enterprise tool stack including Jenkins, GitHub Actions
  • Deployment utilizing containerization solutions like Docker and Kubernetes
  • Experience with AWS cloud services and running Apache Spark applications
Qualifications:
  • Bachelors Degree in any Engineering verticals, Masters Degree is a plus
  • Machine Learning Engineer with 5+ years of experience in designing, building, and maintaining machine learning models and pipelines.
  • Proven ability to work with a variety of data sources, including structured, semi-structured, and unstructured data.
  • Strong experience in Python (MLlib, TensorFlow, and PyTorch). 
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