Senior Applied AI Engineer II

Saint-Gobain Innovation North America is looking for a Senior Applied AI Engineer II to help convert R&D, industrial, scientific, and business challenges into production-ready AI and digital solutions. This onsite role in Northborough, MA centers on hands-on delivery, from problem framing through architecture, deployment, and sustained adoption across enterprise environments.

In this position, you will work directly with domain experts and stakeholders to define high-value use cases, design accountable human-and-AI workflows, and build reliable AI, machine learning, data, automation, and software components. You will also engineer generative AI and large language model systems with evaluation, guardrails, and reliability monitoring.

Responsibilities

• Discover and frame high-value problems by partnering with domain experts and users to understand workflows, decisions, operating conditions, available data, and the appropriate role for AI.
• Translate ambiguous needs into technical requirements, solution architectures, delivery plans, and measurable outcomes.
• Select suitable approaches and design effective human-and-AI workflows where accountable domain experts retain key decisions.
• Design, build, integrate, test, deploy, and support production AI, machine learning, data, automation, and software components using Python, APIs, data pipelines, Git-based development, CI/CD, documentation, observability, and maintainable engineering practices.
• Engineer generative AI and large language model systems using RAG, vector search, prompt and orchestration patterns, agentic workflows, evaluation, guardrails, source traceability, qualified human review, and reliability monitoring.
• Integrate solutions with enterprise data, databases, dashboards, web applications, on-premises or cloud infrastructure, and approved services in coordination with IT, data, MLOps, cybersecurity, architecture, and platform teams.
• Evaluate quality, reliability, usability, limitations, and workflow impact.
• Protect sensitive information by using approved environments and following information-protection, security, legal, export-control, and data-governance requirements, including clear communication when AI is not the right solution.
• Drive deployment, demonstrations, user adoption, documentation, training, operational handoff, and continuous improvement.
• Communicate progress, tradeoffs, risks, value, and next steps clearly to both technical and business audiences.
• Create reusable reference architectures, templates, evaluation methods, integration patterns, and deployment playbooks.
• Provide technical guidance and mentor junior engineers, data scientists, interns, and project contributors.

Requirements

• Bachelor’s degree plus 12 years of experience, Master’s degree plus 7 years of experience, or PhD plus 3 years of experience in Software Engineering, Data Science, Industrial Engineering, Mechanical Engineering, or a related technical discipline.
• Demonstrated hands-on delivery of production software, data, AI/ML, analytics, automation, or generative AI solutions in industrial, enterprise, R&D, or customer-facing environments beyond notebooks and proofs of concept.
• Strong Python and software-engineering skills, including maintainable code, modular design, APIs, data pipelines, testing, Git-based development, and technical documentation.
• Demonstrated experience engineering generative AI and large language model systems using RAG, vector search, orchestration patterns, agentic workflows, model and prompt evaluation, guardrails, and reliability monitoring.
• Experience integrating solutions with enterprise data sources, databases, dashboards, web applications, on-premises or cloud infrastructure, and applicable security and data-governance controls.
• Ability to lead stakeholder discovery, map workflows, and translate ambiguous needs into technical requirements, architectures, delivery plans, success measures, and adoption plans.
• Ability to clearly explain architecture, risks, tradeoffs, limitations, and value to scientists, engineers, industrial teams, business stakeholders, IT, cybersecurity, and leadership.
• Technical leadership across ambiguous projects, including sound engineering judgment, hands-on implementation, cross-team coordination, and the ability to adjust scope and approach as conditions change.
• Demonstrated experience moving solutions from prototype to sustained use, including monitoring, operational handoff, user adoption, continuous improvement, and measurable workflow impact.
• Experience mentoring less-experienced engineers, data scientists, interns, or project contributors in engineering, documentation, evaluation, and deployment practices.

Technologies

• Python
• APIs
• Data pipelines
• Git-based development
• CI/CD
• Observability
• RAG
• Vector search
• Prompt and orchestration patterns
• Agentic workflows
• Evaluation
• Guardrails
• Source traceability
• Qualified human review
• Reliability monitoring

Benefits

• Health and Well-being
• Medical, Prescription Drug, Vision, and Dental Insurance
• Healthcare Saving Account and Flexible Spending Account options
• LiveWell Wellness Program
• Employee Assistance Program (EAP)
• Paid Time Off and Paid Parental Leave
• 401(k) with Company Match
• Retirement Accumulation Plan (RAP) Cash Balance Pension Plan
• Company-provided Life Insurance, AD&D, Short-Term Disability
• Voluntary employee and dependent life insurance, Long-Term Disability, Critical Illness and Accident Insurance
• Group Legal
• Identity Theft Protection
• Auto and Home Insurance
• Pet Insurance and Discounts
• Back-up Child and Eldercare
• PerkSpot Employee Discount Program
• Volunteer Day

Preferred Qualifications

• Experience with cloud platforms, containers, CI/CD, MLOps or LLMOps, observability, or regulated or high-confidentiality data environments.
• Experience in manufacturing, industrial R&D, scientific R&D, process engineering, or technical consulting environments.
• Familiarity with one or more relevant specializations such as computer vision, optimization, simulation workflows, IoT, laboratory or plant data, knowledge graphs, or enterprise search.
• Ability to learn new scientific, manufacturing, or business domains quickly; materials-science experience is helpful but not required.

Compensation

• National pay range: $102,000 to $158,000 per year.
• This position is eligible for an annual bonus.
• Targeted bonus: 13% of base salary based on company and individual performance measures.
• Total Target Cash range (base pay and annual bonus): $115,260 to $178,540.
• Bonus payments are part of variable compensation and may vary based on company and individual performance and are not guaranteed.