Engineer Jobs in Northborough, MA

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Senior Applied AI Engineer II

Saint Gobain Northborough, MA

Northborough, MA

Today

$102k - $158k/yr Full time Senior Artificial Intelligence CI/CD

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Job Description

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.

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