Senior ML Operations Engineer

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 114,000 colleagues serve people in more than 160 countries.

     

JOB DESCRIPTION:

Interested in applying your wealth of technical knowledge and experience towards an opportunity in the medical field and improving the lives of people with diabetes?  The candidate will be responsible for building machine learning and artificial intelligence products and has exceptional skills and experience in

productionizing machine learning and AI models.  

 

The candidate will be working with other data engineers, data analysts and data scientists to focus on applying data engineering, data science and machine learning approaches to solve business problems. As a senior member of the Data Engineering & Analytics team, you will be building machine learning and artificial intelligence products to uncover customer, product and operational insights.  

 

The candidate should have a passion for software engineering to help shape the direction of the team. Highly sought-after qualities include a self-starter, versatility and a desire to continuously learn, improve, and empower other team members. Candidate will support building scalable, highly available, efficient, and secure software solutions for big data initiatives.  

 

 

Responsibilities 

• Designing, architecting and developing machine learning and deep learning systems and platforms.  

• Support the AI Ops needs of data science & software engineering teams from multiple products 

• 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 

• 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  

• Design model performance metrics, retraining schedule and tests 

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

• Create software architecture and design documentation for the supported solutions and overall best practices and patterns 

• Provide architecture and technical knowledge training and support for the solution groups 

• Develop good working relations with the other solution teams and groups, such as Engineering, Marketing, Product, Test, QA. 

• Mentor other engineers and data scientists, remain aware of new developments in the field, and help build and grow the team 

 

 

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