Values

The ad says“AI/ML and generative AI patterns: deep understanding of with modern patterns such as retrieval-based approaches, agent-based orchestration concepts, model evaluation/guardrails fundamentals, and machine learning operations concepts (MLOps).”
The ad says“Architecture & systems thinking: ability to design cohesive, end‑to‑end solutions spanning data ingestion, data products, integrations, user workflows and user experiences.”
The ad says“Familiarity with continuous integration/continuous delivery pipelines, DevOps/DevSecOps practices, and containerization/orchestration concepts (for example: Docker and Kubernetes).”
The ad says“Familiarity with continuous integration/continuous delivery pipelines, DevOps/DevSecOps practices, and containerization/orchestration concepts (for example: Docker and Kubernetes).”
The ad says“Familiarity with continuous integration/continuous delivery pipelines, DevOps/DevSecOps practices, and containerization/orchestration concepts (for example: Docker and Kubernetes).”
The ad says“Familiarity with continuous integration/continuous delivery pipelines, DevOps/DevSecOps practices, and containerization/orchestration concepts (for example: Docker and Kubernetes).”
The ad says“Cloud and full-stack fluency: hand-on experience with cloud infrastructure and services (GCP), including High Performance Compute, AI/ML, and full stack application development.”
The ad says“Cloud and full-stack fluency: hand-on experience with cloud infrastructure and services (GCP), including High Performance Compute, AI/ML, and full stack application development.”
The ad says“AI/ML and generative AI patterns: deep understanding of with modern patterns such as retrieval-based approaches, agent-based orchestration concepts, model evaluation/guardrails fundamentals, and machine learning operations concepts (MLOps).”
The ad says“Cloud and full-stack fluency: hand-on experience with cloud infrastructure and services (GCP), including High Performance Compute, AI/ML, and full stack application development.”
The ad says“Exposure to modeling and simulation, including high performance computing integration patterns.”
The ad says“Familiarity with knowledge graph concepts and/or semantic search patterns.”
The ad says“Familiarity with continuous integration/continuous delivery pipelines, DevOps/DevSecOps practices, and containerization/orchestration concepts (for example: Docker and Kubernetes).”
The ad says“Data & AI architecture thought leadership: deep understanding of metadata strategy, lineage, and semantic modeling concepts (including taxonomies and ontologies) to enable enterprise reuse and AI readiness.”
The ad says“AI/ML and generative AI patterns: deep understanding of with modern patterns such as retrieval-based approaches, agent-based orchestration concepts, model evaluation/guardrails fundamentals, and machine learning operations concepts (MLOps).”
The ad says“Data & AI architecture thought leadership: deep understanding of metadata strategy, lineage, and semantic modeling concepts (including taxonomies and ontologies) to enable enterprise reuse and AI readiness.”
The ad says“Exposure to modeling and simulation, including high performance computing integration patterns.”
The ad says“Data & AI architecture thought leadership: deep understanding of metadata strategy, lineage, and semantic modeling concepts (including taxonomies and ontologies) to enable enterprise reuse and AI readiness.”
The ad says“AI/ML and generative AI patterns: deep understanding of with modern patterns such as retrieval-based approaches, agent-based orchestration concepts, model evaluation/guardrails fundamentals, and machine learning operations concepts (MLOps).”
The ad says“Security and intellectual property-aware design: ability to incorporate security-by-design principles and partner effectively with cybersecurity and enterprise architecture stakeholders.”
The ad says“Data & AI architecture thought leadership: deep understanding of metadata strategy, lineage, and semantic modeling concepts (including taxonomies and ontologies) to enable enterprise reuse and AI readiness.”
The ad says“Familiarity with knowledge graph concepts and/or semantic search patterns.”
The ad says“Data & AI architecture thought leadership: deep understanding of metadata strategy, lineage, and semantic modeling concepts (including taxonomies and ontologies) to enable enterprise reuse and AI readiness.”

Job Title:

Lead, Solution Architect - R&D Digital Platforms

Job Description:

Here at Entegris, we use advanced science to enable technologies that transform the world, and we are seeking employees who have the drive to continue that mission.

Please note: This will be a hybrid position, and will be located in Bedford, MA.

The Role:

Our team is looking for a highly skilled Lead, Solution Architect - R&D Digital Platforms with experience envisioning and designing end‑to‑end architectures for digital platforms, data, AI/ML, and user experience capabilities, enabling scalable, secure, and reusable solutions across complex research and development environments.

Our Digital Innovation Platform leverages advances in artificial intelligence and machine learning, modeling and simulation, and cloud technologies to deliver digital tools and workflows that accelerate innovation and reduce cycle time for new product development.

In this role, you will define and evolve the architectural foundation that enables globally scalable, secure, and reusable digital capabilities for research and development teams. You will ensure that technology components are designed cohesively across data ingestion, data products, integrations, and user workflows—while enabling enterprise reuse and long-term sustainability. You will work closely with Product Owners, delivery teams, enterprise architecture and cybersecurity partners, and external vendors to guide architecture decisions from concept through delivery.

What You’ll Do:

Define and evolve Digital Innovation Platform solution architecture, including target‑state blueprints and reusable patterns spanning experimentation workflows, data services, integrations and experience-layer design enabling digital R&D capabilities.

Design integration and data architectures supporting experiment execution, data capture, contextualization, data lineage, and enterprise reuse (for example: laboratory systems integration, modern cloud data platforms, and semantic approaches).

Guide AI‑enabled capability architecture, including search, summarization, and workflow orchestration aligned to enterprise guardrails.

Evaluate and recommend build vs. buy vs. integrate approaches, balancing speed, scalability, maintainability, and total cost of ownership.

Provide architectural guidance and governance across delivery teams and partners through design reviews, architecture decision records, and standards/patterns enforcement.

Ensure alignment with cybersecurity, intellectual property protection, compliance, and enterprise architecture standards in partnership with security and enterprise architecture teams.

Define non-functional requirements and operational patterns (scalability, reliability, performance, observability) to support global adoption and sustained platform operations.

Enable secure delivery practices (DevOps/DevSecOps), advising on continuous integration and continuous delivery pipelines, infrastructure-as-code patterns, and runtime considerations (including containerization where appropriate).

Coordinate cross-domain alignment by partnering with enterprise data teams to ensure platform data and semantic models integrate cleanly with adjacent domains (for example: quality, supply chain, and manufacturing) where required.

Promote architectural consistency and reuse by sharing best practices and driving adoption of common patterns across initiatives.

What We Seek:

Bachelor’s degree in Computer Science, Engineering, or related field (advanced degree preferred).

8+ years of experience in solution architecture and delivering cloud-based platforms and custom solutions.

Experience with digital research and development (R&D) ecosystems and laboratory informatics (for example: Laboratory Information Management Systems, Electronic Lab Notebooks, scientific data management systems, instrumentation integration).

Strong depth across systems integration, cloud platforms, data and analytics architectures, and AI/ML concepts.

Experience designing solutions that support research and development, engineering, manufacturing, or other scientific/technical workflows.

Experience working in agile delivery environments, including scaled agile (SAFe) models, across globally distributed teams and partners.

Preferred Qualifications:

Exposure to modeling and simulation, including high performance computing integration patterns.

Familiarity with knowledge graph concepts and/or semantic search patterns.

Familiarity with continuous integration/continuous delivery pipelines, DevOps/DevSecOps practices, and containerization/orchestration concepts (for example: Docker and Kubernetes).

Experience operating in federated, global environments with diverse processes, systems, and data landscapes.

Outstanding Candidates Will Have:

Architecture & systems thinking: ability to design cohesive, end‑to‑end solutions spanning data ingestion, data products, integrations, user workflows and user experiences.

Data & AI architecture thought leadership: deep understanding of metadata strategy, lineage, and semantic modeling concepts (including taxonomies and ontologies) to enable enterprise reuse and AI readiness.

AI/ML and generative AI patterns: deep understanding of with modern patterns such as retrieval-based approaches, agent-based orchestration concepts, model evaluation/guardrails fundamentals, and machine learning operations concepts (MLOps).

Technical judgment & tradeoffs: ability to evaluate options and clearly communicate architectural recommendations.

Security and intellectual property-aware design: ability to incorporate security-by-design principles and partner effectively with cybersecurity and enterprise architecture stakeholders.

Communication & documentation: ability to create clear artifacts (diagrams, decision records, non-functional requirements) and communicate effectively to technical and non-technical audiences.

Cloud and full-stack fluency: hand-on experience with cloud infrastructure and services (GCP), including High Performance Compute, AI/ML, and full stack application development.

What We Offer:

Our total rewards package goes above and beyond just a paycheck. Whether you’re looking to build your career, improve your health, or protect your wealth, we offer generous benefits to help you achieve your goals.

Compensation: $140,000.00-$175,000.00 a year range with actual pay dependent on candidate overall skills for the role

Annual bonus eligible
Progressive paid time off policy that empowers you to take the time you need to recharge
Generous 401(K) plan with an impressive employer match with no delayed vesting
Excellent health, dental and vision insurance packages to fit your needs
Education assistance to support your learning journey
A values-driven culture with colleagues that rally around People, Accountability, Creativity and Excellence
Company anticipates the application window closing approximately 5 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require Company to shorten or extend the application window

Entegris does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need Entegris immigration sponsorship (e.g., H1B, TN, STEM OPT, etc.) now or in the future.

At Entegris we are committed to providing equal opportunity to all employees and applicants. Our policy is to recruit, hire, train, and reward employees for their individual abilities, achievements, and experience without regard to race, color, religion, sexual orientation, age, national origin, disability, marital or military status.

Asks for

Values