Principal Artificial Intelligence (AI) Solutions Architect

We are seeking a highly experienced, hands-on Principal Artificial Intelligence (AI) Solutions Architect to define, build, and govern Sensata’s enterprise AI technical foundation. This role is expected to combine enterprise architecture judgment with practical implementation ability; the person must be comfortable moving between strategy, design, prototyping, vendor challenge, and production-readiness review

This is not a purely strategic or oversight role. You will be expected to actively design, prototype, and guide implementation of production AI systems in complex enterprise environments. This role will operate under the direction of the Artificial Intelligence Leader (Head of AI) and will support enterprise AI priorities across functions, including but not limited to engineering, procurement, supply chain, operations, legal, quality, and commercial teams

You will establish the AI reference architecture that ensures scalable, reusable, and integrated solutions across the business—while also staying close to execution, validating designs through real implementations. Ideally you have personally deployed production AI systems, understands modern GenAI architectures (LLMs, RAG, agents), and are comfortable working directly with code, data, and enterprise systems.

This role is critical in preventing fragmented, vendor-driven solutions and ensuring AI is built once and scaled effectively across use cases.

General Responsibilities

• •Define and maintain the AI architecture roadmap, including platforms, tools, frameworks, integration patterns, and reusable design standards.
• •Translate complex business needs into scalable AI solution architectures that align with enterprise technology, data, security, privacy, and compliance requirements.
• •Design end-to-end AI systems, including data pipelines, model development workflows, model deployment, monitoring, evaluation, and lifecycle management.
• •Establish architectural guardrails for generative AI, machine learning, large language models, retrieval-augmented generation, intelligent agents, APIs, and model integration patterns.
• •Partner with engineering, data science, product, security, legal, compliance, and business stakeholders to ensure AI solutions are reliable, responsible, and fit for purpose.
• •Guide teams in implementing, testing, governance, and cost optimization practices for production AI systems.
• •Evaluate emerging AI technologies, vendors, and frameworks and recommend adoption strategies that balance innovation, risk, scalability, and long-term maintainability.
• •Provide technical leadership, mentorship, and architectural review for teams building AI-enabled products, platforms, and internal capabilities.
• •Drive consistency and reuse by creating reference architectures, implementation playbooks, design patterns, and technical documentation.
• •Communicate AI architecture strategy, tradeoffs, risks, and opportunities to senior technology and business leaders.

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 :

• •Prototype and deliver hands-on AI solutions, integrating with enterprise systems (ERP, MES, PLM, APIs)
• •Build advanced AI capabilities (RAG, LLM workflows/agents, document intelligence, enterprise search)
• •Establish AI-specific data architecture standards (semantic models, metadata, taxonomy, lineage, access)
• •Implement production-readiness practices (evaluation datasets, hallucination testing, human-in-loop, observability)
• •Lead technical vendor validation and build vs. buy analysis through prototyping and benchmarking
• •Create reusable accelerators (modular components, decision records, implementation assets)
• •Prioritize AI use cases based on business value, scalability, risk, and speed
• •Execute effectively in ambiguous, fast-moving environments with close alignment to delivery

Additional Qualifications :

• •8+ years delivering production AI/ML solutions with hands-on engineering experience
• •Expertise in LLMs, RAG, vector databases, and agent-based systems
• •Experience with AI frameworks/tools (LangChain, LlamaIndex, Semantic Kernel, OpenAI/Azure OpenAI)
• •Strong background in enterprise AI integration and data ecosystems
• •Proven ability to lead vendor evaluation and build vs. buy decisions
• •Preferred: Experience with Microsoft/Azure AI stack (Azure OpenAI, AI Search, Fabric, Power Platform)
• •Demonstrated professional proficiency in English language, both verbal and written

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