AI Data Solutions Engineer

Work Schedule Standard (Mon-Fri)

Environmental Conditions Office

Job Description

Position Summary As part of the Thermo Fisher Scientific Corporate Digital team, you'll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every day by enabling customers to make the world healthier, cleaner, and safer. The Corporate Digital team is seeking an innovative, technically skilled, and solutions-oriented AI Solutions Engineer to accelerate the adoption of generative AI across Sales and Marketing. This role combines applied data science, AI engineering, and advanced analytics to evaluate AI solutions, assess enterprise data readiness, and develop AI-enabled data products that improve business outcomes. Working closely with business stakeholders, product managers, and technical teams, you will evaluate large language model (LLM) applications OpenAI agents to ensure they deliver accurate, relevant, and measurable business value. You will assess and prepare enterprise Sales and Marketing data for AI consumption by improving data quality, governance, structure, and accessibility while delivering insights that support strategic decision-making.

Primary Responsibilities

• Evaluate generative AI agents, copilots, and LLM-based applications for accuracy, relevance, consistency, and business value.
• Develop AI evaluation methodologies, benchmarks, scorecards, and success metrics.
• Perform qualitative and quantitative assessments of AI-generated responses and recommend improvements.
• Partner with AI developers to improve prompts, retrieval quality, and overall AI performance.
• Assess enterprise Sales and Marketing data for AI readiness, including quality, governance, metadata, and usability.
• Prepare structured and unstructured data to support AI, analytics, and intelligent search.
• Design scalable datasets and data models that improve AI retrieval and business insights.
• Analyze Sales and Marketing data to identify trends and actionable recommendations.
• Develop dashboards, reporting, and analytical models using Python, SQL, Databricks, and Power BI.
• Collaborate with business and technical stakeholders to translate requirements into AI-enabled solutions.
• Document evaluation methodologies, data standards, and best practices.
• Evaluate emerging AI technologies and recommend improvements to enterprise AI capabilities.