Job Description

Title: DevOps/MLOps Engineer (Remote Opportunity)

Location: Remote / Field US

JobType: Full-Time
Category: IT

How Will You Make an Impact?

The Dev/MLOps Engineer will play a leading strategic role within Thermo Fisher Scientific s Service Innovation Data Science team who will develop, deploy, and maintain innovative machine learning solutions. This role will push the boundaries of remote instrument technology through optimization of cloud ecosystems and complex data integration

An ideal candidate possesses an in-depth understanding of machine learning, DevOps/MLOps, and data engineering practices

This position is hands-on, highly technical, and requires extensive partnerships among business unit SMEs to enable data connectivity across our instrument products and pioneer novel technologies.

What will you do?

  • Collaborate across Thermo Fisher groups, divisions, and business units to enable and align digital transformation strategies to improve company-wide business practices and processes
  • Support AI/ML deployment: Build, lead, optimize, monitor, and validate machine learning models; deploy to production systems reliably and efficiently through direct collaboration with our data sciences, digital engineering, and IT partners
  • Data science model support: handle cloud-based development environment, perform code refactoring and optimization, containerization, deployment, versioning, quality monitoring, and automation
  • Communicate results and share ideas across the team to improve algorithm development and build innovative ways to drive service excellence

Keys to Success

  • Minimum Bachelor’s Degree with minimum 4+ years of experience in computer engineering, computer science, machine learning, analytics, statistics, applied math or related; or Graduate Degree with 2+ years of experience
  • Excellent written and oral communication skills with colleagues across the organization
  • Consistent track record in delivering scalable solutions supporting the deployment and maintenance of data science and machine learning-based software applications
  • Expert DevOps knowledge of data architectures, data pipelines, real-time data processing, streaming, networking, security, and CI/CD
  • Data Science stack proven understanding: Python, Databases (SQL Server), GitHub, AWS (EC2, S3, Lambda, RDS), APIs, cluster management (Databricks, Spark), container orchestration (Kubernetes, Docker)
  • Data modeling and algorithm development built on ML, AI
  • Technical project or product management, SAP C4C, SFDC, front-end software development