Machine Learning/AI Engineer at Randstad USA

7% Bonus + 10k annual stock + Free Insurance + Pension

Chicago | Texas | Sylmar, CA

4 days onsite

ML/AI Engineer

• Career development with an international company where you can grow the career you dream of .
  
  
• Free medical coverage for employees* via the Health Investment Plan (HIP) PPO
  
  
• An excellent retirement savings plan with high employer contribution
  
  
• Tuition reimbursement, Save education debt program and FreeU education benefit - an affordable and convenient path to getting a bachelor's degree.
  
  
• A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune.
  
  
• A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working women, female executives, and scientists.
  
  

At Client, we're committed to helping people live their best possible life through the power of health. For more than 125 years, we've brought new products and technologies to the world -- in nutrition, diagnostics, medical devices and branded generic pharmaceuticals -- that create more possibilities for more people at all stages of life. Today, 99,000 of us are working to help people live not just longer, but better, in the more than 150 countries we serve.

We are looking for a savvy AI Engineer to help us build machine learning and artificial intelligence products. The ideal candidate has exceptional skills and experience in productionizing machine learning and AI models. This individual will be responsible for designing, architecting and developing machine learning and deep learning systems and platforms. They will be self-directed and comfortable supporting the AI Ops needs of data science & software engineering teams from multiple products. The right candidate will be excited by the prospect of applying machine learning and AI Ops to support Clientt's next generation of products and data initiatives through thought leadership and application.

What You'll Work On

Support the growing ML and AI Ops initiatives at Clientt

Help in architecting ML and AI platforms for various divisions and teams

Develop and apply industry leading machine learning and deep learning systems to help solve business problems

Ability to customize large language models for product applications, and knowledgeable in natural language processing and generative AI

Lead design and coding of big data and machine learning systems

Collaborate with product stakeholders to ideate and prove viability of machine learning use cases (Ability to translate business needs and goals into an AI approach and solution, and articulate findings to a non-technical audience)

Effective advanced analytics and AI skills with a foundation in programming (e.g. R, python), database environment (e.g. big-data platforms and SQL skills), and dashboard development (e.g. PowerBI, QlickView)

Design model performance metrics, retraining schedule and tests

Assist with deploying models to public cloud infrastructure such as AWS and Microsoft Azure

Mentor other engineers and data scientists, remain aware of new developments in the field, and help build and grow the team (and other duties as assigned)

Required Qualifications

Bachelor's degree in computer science or related field

5 years of leading design and architecture of new and existing machine learning systems

5 years of experience of professional software development

Experience with natural language processing and generative AI

Preferred Qualifications

Graduate degree in Computer Science, Mathematics, or another quantitative field

Experience in time series data, signal, image, and video processing

Deep knowledge of math, probability, statistics, and algorithms

Outstanding analytical and problem-solving skills

Prior experience in the healthcare or other regulated industries

Experience using the following software/tools:

Unsupervised, semi-supervised and supervised learning methods

Machine learning frameworks such as Keras, PyTorch, or Tensorflow

Libraries such as numpy, scikit-learn, scipy and statsmodel

Programming languages: Python, R, .NET Core, Java