AI Architect (Ref: 197969)

About Us

Operating within chemicals and related products manufacturing, our client develops and produces materials that support industrial supply chains and demanding commercial applications. Its work combines process discipline, technical expertise, and operational reliability across environments where quality, safety, and consistency are essential.

Digital capability is increasingly important to this organisation’s manufacturing performance, from improving production insight and engineering decision-making to strengthening the use of data across complex operations. The AI Architect will help translate that opportunity into practical, secure, and scalable solutions with measurable business value.

Job Description

The AI Architect will define and lead the technical direction for artificial intelligence across a manufacturing environment. This role will connect business priorities with enterprise architecture, identifying where machine learning, generative AI, advanced analytics, and intelligent automation can improve productivity, quality, reliability, safety, and decision-making.

Success will involve more than producing concepts or prototypes. You will establish architectures that can move into dependable production, align stakeholders around realistic delivery plans, and create the standards, controls, and operating practices required for responsible AI adoption across the organisation.

Working with engineering, operations, data, technology, and business leaders, you will shape solutions from discovery through implementation and continuous improvement. The position requires strategic perspective alongside sufficient technical depth to challenge designs, guide delivery teams, and ensure that AI capabilities perform effectively in real operational settings.

Based in Boston, Massachusetts, this opportunity suits an experienced architect who can operate across corporate and plant-level priorities. Your contribution will influence how intelligent technologies are selected, integrated, governed, measured, and scaled throughout the manufacturing portfolio.

Key Responsibilities

• Define the target architecture and multi-year roadmap for AI, machine learning, generative AI, and intelligent automation capabilities.
• Assess manufacturing, supply chain, quality, maintenance, engineering, and commercial use cases to prioritise investments with clear operational or financial outcomes.
• Design secure, scalable solutions that integrate enterprise systems, industrial data sources, cloud services, analytics platforms, and operational technologies.
• Establish patterns for data preparation, model development, deployment, monitoring, evaluation, retraining, and lifecycle governance.
• Guide the responsible application of large language models, computer vision, predictive analytics, optimisation, and other relevant AI techniques.
• Translate complex technical options into clear recommendations for senior executives, plant leaders, product owners, and delivery teams.
• Set architectural principles covering interoperability, cybersecurity, privacy, resilience, explainability, model risk, and regulatory expectations.
• Partner with data and technology teams to improve the quality, accessibility, lineage, and reliability of information used by AI systems.
• Oversee proof-of-concept decisions and create practical pathways for successful solutions to progress into production.
• Define success measures for AI initiatives, including adoption, accuracy, cycle time, cost reduction, quality improvement, and user experience.
• Review vendor platforms, foundation models, engineering tools, and integration approaches against business and technical requirements.
• Build reusable reference architectures, standards, and delivery methods that accelerate future AI initiatives without compromising control.
• Identify architectural risks, dependencies, and capability gaps, then lead mitigation plans with relevant stakeholders.
• Promote knowledge sharing and strengthen the organisation’s ability to deliver and operate AI solutions sustainably.

Requirements

• Extensive experience in enterprise architecture, AI architecture, machine learning engineering, data platforms, or a closely related discipline.
• Demonstrated delivery of production-grade AI or advanced analytics solutions, preferably within manufacturing, chemicals, industrial, or other asset-intensive environments.
• Strong understanding of modern AI capabilities, including generative AI, large language models, predictive modelling, computer vision, optimisation, and intelligent automation.
• Practical knowledge of cloud architecture, data platforms, APIs, distributed systems, integration patterns, and software engineering standards.
• Experience designing solutions that connect enterprise applications with plant, operational technology, sensor, laboratory, or industrial control data.
• Proficiency with architecture methods, technology roadmaps, reference models, design governance, and technical decision documentation.
• Familiarity with MLOps and the operational requirements of model deployment, observability, performance management, security, and continuous improvement.
• Understanding of responsible AI principles, including explainability, bias management, privacy, access control, model risk, and human oversight.
• Ability to evaluate emerging technologies and distinguish scalable business solutions from short-term experimentation.
• Strong commercial judgement, with the ability to connect technical investment to measurable manufacturing, customer, and enterprise outcomes.
• Experience influencing senior stakeholders and leading through a matrix structure without relying solely on direct authority.
• Clear written and verbal communication skills, including the ability to explain technical subjects to non-technical audiences.
• Evidence of delivering complex programmes across multiple functions, locations, platforms, or business units.
• A bachelor’s or master’s degree in computer science, engineering, data science, artificial intelligence, or a related field is advantageous.
• Exposure to regulated, safety-critical, quality-controlled, or highly governed operating environments would be valuable.

Benefits

• Strategic influence over how AI is adopted across a technically complex manufacturing organisation.
• Opportunity to connect emerging AI capabilities with tangible improvements in production, quality, reliability, and operational performance.
• Broad exposure to enterprise technology, industrial operations, data, engineering, and senior business leadership.
• Scope to establish architecture standards and delivery practices that will shape future investment and scale.
• Access to challenging use cases where responsible technology design has direct commercial and operational significance.
• A visible role in building durable internal AI capability rather than delivering isolated experiments.
• Professional growth through work spanning architecture, innovation, governance, transformation, and industrial technology.
• A Boston-based opportunity within an organisation focused on applying advanced technology to real-world products and operations.

Other

This role is intended for an architect who can balance innovation with operational discipline. Candidates should be comfortable asking difficult questions about value, feasibility, risk, adoption, and long-term ownership before recommending a technical direction.

The strongest applicants will combine hands‑on understanding of modern AI with enterprise-level judgement and the ability to build trust across technical and operational communities. Experience in chemicals, manufacturing, industrial technology, or similarly complex environments will help you make an immediate and relevant contribution.