Senior Staff AI / ML Solutions Engineer

Thermo Fisher Scientific•Morrisville, NC

1d•Remote

About The Position

Thermo Fisher Scientific’s Clinical Research Group (CRG) is hiring a Senior Staff AI/ML Solutions Engineer to support the Finance organization. This senior individual-contributor role serves as the hands-on technical lead for AI initiatives that improve finance business processes, decision-making, and productivity. The engineer translates business needs into practical AI solutions and leads technical delivery from discovery and architecture through development, evaluation, deployment, and ongoing improvement. The role provides technical direction and is accountable for project outcomes, collaborating across business and technology teams and mentoring junior colleagues. People management may be included as business needs evolve, while hands-on technical leadership remains the primary responsibility. This is a remote position for qualified candidates in the United States, with a preference for Eastern or Central time zones. The role may require as-needed travel of 0–20%. Relocation assistance is not provided. Candidates must be legally authorized to work in the United States without sponsorship and must pass a comprehensive background check, including drug screening. The estimated annual salary range in North Carolina is $138,000–$208,000, with possible eligibility for a variable annual bonus based on company, team, and/or individual performance in accordance with company policy.

Requirements

Advanced degree and 6 years of experience in computer science, data science, engineering, or a related technical field and AI experience; or a bachelor’s degree and 8 years of experience in those fields and AI experience.
Demonstrated experience serving as the technical lead for AI or advanced analytics projects, including delivery into production.
Strong hands-on proficiency in Python and SQL, with experience building maintainable applications and data integrations.
Practical experience developing generative AI solutions using platforms such as Azure OpenAI or Amazon Bedrock.
Experience integrating AI models with enterprise data and applications, including retrieval-augmented generation, APIs, and workflow orchestration.
Ability to evaluate AI solution quality and explain limitations, technical tradeoffs, and deployment considerations.
Experience with cloud services and data platforms such as AWS, Azure, Snowflake, or Databricks.
Working knowledge of data architecture, ETL/ELT processes, data quality, and access controls.
Experience using version control, testing, CI/CD, and collaborative development tools.
Strong communication skills and the ability to translate business needs into technical requirements and delivery plans.
Demonstrated ability to guide technical contributors and coordinate delivery across functions.
Able to communicate, receive, and understand information and ideas with diverse groups of people in a comprehensible and reasonable manner.
Able to work upright and stationary for typical working hours.
Ability to use and learn standard office equipment and technology with proficiency.
Able to perform successfully under pressure while prioritizing and handling multiple projects or activities.
Must be legally authorized to work in the United States without sponsorship.
Must be able to pass a comprehensive background check, including a drug screening.

Nice To Haves

Experience applying AI to processes, analytics, or enterprise productivity.
Experience with distributed data processing technologies such as PySpark, Apache Spark, and Delta Lake.
Experience designing and implementing enterprise data models and semantic layers that support consistent business definitions, analytics, and AI applications.
Experience with predictive modeling and statistical analysis.
Experience using Azure DevOps or JIRA within Agile delivery teams.

Responsibilities

Lead technical delivery of AI projects, defining solution architecture, implementation plans, dependencies, and acceptance criteria.
Partner with finance and other business stakeholders to identify valuable AI use cases, assess feasibility, and establish measurable outcomes.
Design, build, and integrate AI applications using generative AI, machine learning, enterprise data, and workflow automation as appropriate.
Develop prototypes and production solutions, selecting technologies based on accuracy, reliability, security, scalability, performance, and cost.
Establish evaluation approaches for AI outputs, including quality, robustness, and appropriate human review.
Design and implement data pipelines, APIs, and enterprise integrations needed to support AI solutions.
Collaborate with IT, architecture, cybersecurity, privacy, and data governance teams to meet applicable requirements throughout development and deployment.
Apply sound software engineering practices, including version control, code review, testing, documentation, and automated deployment.
Define monitoring, support, and operational handoff requirements; investigate issues and improve deployed solutions.
Communicate technical recommendations, tradeoffs, project risks, and results to technical and business audiences.
Mentor junior data scientists and engineers through design reviews, code reviews, and collaborative problem-solving.