Expert AI Architect

sensata; Attleboro (MA); On-site; USD 180,000 - 240,000; Full time

Job summary

Sensata seeks an Expert AI Architect to define and drive enterprise AI standards, architecture, and governance across platforms, models, and integrations. The role partners with engineering, cybersecurity, and business teams to ensure scalable, secure, and responsible AI implementations.

The candidate will own the Azure-based AI reference architecture, guide multi-team delivery, and shape the multi-year AI roadmaps while balancing risk, cost, and impact.

Qualifications

• University degree or equivalent experience in software/AI/architecture.
• Strong communication and presentation skills.
• Ability to translate complex priorities into scalable AI architectures.
• Experience with security, privacy, and regulatory compliance.

Responsibilities

• Own the enterprise AI reference architecture and Azure platform blueprint.
• Define Dev/Test/Prod patterns and infrastructure-as-code for AI solutions.
• Build reference implementations, templates, and reusable components.
• Lead architecture reviews and critical buy/build decisions for AI solutions.
• Set standards for model selection, evaluation, and production readiness.

Skills

AI architecture

Azure

LLMOps

Communication

Governance

Education

Bachelor's degree

Tools

Azure AI Foundry

Terraform

Bicep

API management

Job description

The Expert AI Architect serves as the enterprise AI architecture authority, defining standards, frameworks, and best practices for scalable, secure, and responsible AI solutions. This role drives architectural consistency across AI platforms, agents, models, integrations, and governance while guiding technical direction across engineering, IT, cybersecurity, and business teams.

General Responsibilities

• Own and evolve the enterprise AI architecture vision and multi-year roadmap, setting direction for platforms, tools, frameworks, integration patterns, and reusable standards across diverse business and technology environments.
• Lead the translation of highly complex, ambiguous business priorities into scalable AI architectures that shape enterprise technology and data strategy while meeting security, privacy, regulatory, and compliance requirements.
• Architect and provide authoritative direction for end-to-end AI ecosystems spanning data pipelines, model development, deployment, observability, evaluation, lifecycle management, and integration with critical enterprise platforms.
• Establish enterprise-wide architectural principles, governance frameworks, and decision criteria for generative AI, machine learning, large language models, retrieval-augmented generation, intelligent agents, APIs, and model integration, resolving the most complex or novel design challenges.
• Influence enterprise priorities and investment decisions by building alignment among engineering, data science, product, cybersecurity, legal, compliance, and senior business stakeholders on responsible, resilient, and value-driven AI solutions.
• Set technical direction and guide multiple teams through implementation, validation, governance, operational readiness, and cost optimization for business-critical production AI systems.
• Anticipate industry and technology shifts; assess emerging AI capabilities, vendors, and frameworks; and define adoption strategies that balance innovation, enterprise risk, scalability, interoperability, and long-term sustainability.
• Serve as the enterprise authority and escalation point for AI architecture, providing expert consultation, design review, and mentorship to senior engineers, architects, data scientists, and technical leaders across the organization.

Experience / Qualifications

• A university degree required (i.e. Bachelors degree) or equivalent relevant work experience.
• Must be a team player able to work in a fast-paced environment with demonstrated ability to handle multiple competing tasks and demands
• Strong communication skills; oral, written and presentation
• Strong organization, planning and time management skills to achieve results
• Strong personal and professional ethical values and integrity
• Holds self-accountable to achieving goals and standards
• Proficient in Microsoft Office programs (Outlook, Word, PowerPoint, and Excel)
• Strong interpersonal & collaboration skills to work effectively with all levels of the organization including suppliers and/or external customers

Additional Responsibilities :

• Own the enterprise AI reference architecture and platform blueprint on Azure, covering agents, RAG, model services, the LLM gateway, identity, observability, and cost controls.
• Define the patterns for Dev/Test/Prod environments, CI/CD, and infrastructure-as-code across AI solutions, and guide the AI Platform Lead in implementing them.
• Build reference implementations, templates, and reusable components hands-on to prove out patterns and speed up delivery by AI engineers and data scientists.
• Serve as design authority for internal and third-party AI solutions, leading architecture reviews and build/buy/reuse decisions.
• Set standards for model selection, evaluation, LLMOps/MLOps, security guardrails, and production readiness.
• Own the AI platform architecture and standards, partnering with IT on the underlying cloud, network, and identity foundations.
• Design how priority AI use cases across supply chain, commercial, quality, and engineering connect to core enterprise systems so they scale from pilot to production.

Additional Qualifications :

• 12+ years of experience in software engineering, data, AI engineering, or enterprise architecture, including recent hands-on delivery of generative AI solutions.
• Proven record of shipping GenAI/LLM solutions (agents, RAG, copilots) into production at enterprise scale.
• Deep expertise in Azure, including Azure AI Foundry, Azure OpenAI, Entra ID, and API management.
• Strong hands-on experience with CI/CD, infrastructure-as-code (e.g., Bicep, Terraform), and LLMOps/MLOps practices.
• Experience establishing architecture standards, design reviews, and reusable patterns adopted across multiple teams.
• Solid understanding of AI security, data governance, privacy, and responsible AI controls.
• Ability to influence senior business and technical leaders and turn business priorities into practical architecture decisions.
• Experience in manufacturing, industrial, or supply chain environments pref