AI Solutions Architect (34385)

Job Summary

The AI Solutions Architect is the senior technical leader for KLS Martin's enterprise AI program and the technical partner to IT leadership and the AI Program Manager. This role designs and builds the foundational AI platforms, services, and reference architectures that power AI adoption across the company, sets the technical standards that federated teams build to, and ensures every AI implementation meets enterprise requirements for security, scalability, reliability, and regulatory compliance.

This is a hands-on architect role: the person in this seat personally designs and ships the foundational systems and the highest leverage production use cases and grows technical leadership responsibilities as the AI engineering team expands.

The AI Solutions Architect partners with stakeholders across IT, business units, Quality, Regulatory, Information Security, and the AI Program Manager to ensure AI solutions deliver on their objectives while meeting applicable regulatory, quality, and security standards.

The AI Solutions Architect contributes to the continuous improvement of AI development practices, reference architectures, evaluation frameworks, and developer enablement, and develops the capability of the AI engineering team through coaching, mentoring, and targeted assignments as the team grows.

Essential Functions, Duties, and Responsibilities
• Personally design, build, and ship foundational AI systems, platforms, and integrations, including agent frameworks, evaluation infrastructure, and the first wave of production use cases.
• Lead end to end implementation of the most strategic AI initiatives across the company.
• Maintain hands on proficiency with the production stack: Python, FastAPI, React, Azure services, and modern AI tooling.
• Define, document, and evolve KLS Martin's reference architectures for the most common AI patterns: retrieval augmented generation, autonomous agents, document processing, classification, and assistive copilots.
• Build and curate the internal prompt and pattern library so federated teams reuse rather than rebuild.
• Establish coding standards, security controls, evaluation requirements, and documentation expectations for AI development across the enterprise.
• Contribute to the AI technology roadmap in partnership with IT leadership and the AI Program Manager, including model providers, platforms, frameworks, and supporting tooling.
• Lead build versus buy decisions for AI capabilities, including technical evaluation of vendor solutions against internal builds
• Provide the technical backbone for vendor selection, proof of concept work, and contract negotiation.
• Design and operate the MLOps and LLMOps stack, including deployment pipelines, model and prompt versioning, evaluation harnesses, monitoring, and observability.
• Build evaluation and testing frameworks, including eval datasets, regression testing, and red team testing for prompt injection, jailbreaks, and data exfiltration.
• Implement cost and token observability for API spend management.
• Lead architectural and technical response to AI specific incidents such as production hallucinations, data leakage, prompt injection, and model availability issues.
• Design and implement identity, secrets, and access management patterns for AI systems, including agent identities, least privilege patterns, and integration with Microsoft Entra ID.
• Ensure data classification, retention, and access controls are designed into every solution from the start.
• Partner with Information Security on threat modeling and risk reviews for new AI systems.
• Ensure AI architectures meet validation requirements when use cases touch quality processes or regulated data under ISO 13485 and FDA 21 CFR Part 11.
• Provide architecture review, code review, and technical mentoring for federated developers and citizen builders working on AI features in their own areas.
• Lead, mentor, and develop AI engineers added to the team over time, including direct people management responsibilities as the team grows.
• Maintain a graduation process for moving prototypes from POC into supported production systems, including handoff criteria, runbooks, and on call expectations.
• Represent AI architecture in enterprise technology decisions and contribute to the broader architecture direction at KLS Martin.
• Partner with IT leadership and the AI Program Manager on use case intake, scoping, prioritization, and technical feasibility.
• Partner with IT Infrastructure, Information Security, and enterprise architecture teams to ensure AI systems fit within the broader technology landscape.
• Coordinate with KLS Germany counterparts on shared architecture standards and reusable patterns.
• Perform other related duties as assigned.

Education and Experience Requirements
• Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field, or equivalent professional experience.
• 8+ years of software engineering experience, with at least 3 years in a Senior, Staff, Principal, or Architect level role.
• Demonstrated track record of personally designing and shipping production AI or machine learning systems.
• Strong proficiency in modern web stacks (FastAPI, Node.js, React), with current and demonstrated ability to ship production code, not just design systems.
• Hands on experience with at least one major model provider API (Anthropic, OpenAI, Azure OpenAI) and the surrounding tooling such as vector databases, embedding models, and agent frameworks.
• Deep experience with cloud architecture (Azure preferred), CI/CD, and DevOps practices, including Azure DevOps.
• Strong working knowledge of authentication, authorization, secrets management, identity, and data security patterns.
• Demonstrated experience mentoring engineers and influencing technical decisions across teams.
• Microsoft Azure Solutions Architect Expert certification, or equivalent cloud architect credential, preferred.
• TOGAF, SAFe, or equivalent enterprise architecture credential preferred.
• Prior experience in a regulated industry (medical device, pharmaceutical, biotechnology, or other FDA regulated environment) preferred.
• Familiarity with Azure OpenAI, Azure AI Foundry, Cosmos DB, and Microsoft Entra ID preferred.
• Experience implementing evaluation frameworks, red team testing, or applied AI safety practices preferred.
• Background in MLOps tooling such as LangSmith, Langfuse, Weights and Biases, MLflow, or equivalent observability platforms preferred.
• Prior people management or team leadership experience preferred.
• Familiarity with enterprise IT domains relevant to the role, including infrastructure, enterprise applications (ERP, HRIS, CRM, QMS), and cybersecurity preferred.

Knowledge, Skills, and Abilities
• Strong written and verbal communication skills, with the ability to influence peers, senior stakeholders, and external vendors.
• Ability to develop and maintain collaborative relationships across IT, business units, Quality, Regulatory, Information Security, and external partners.
• Deep technical knowledge of modern AI and machine learning systems, including large language models, retrieval augmented generation, autonomous agents, and supporting infrastructure.
• Strong understanding of cloud architecture, distributed systems, and modern software engineering practices.
• Knowledge of secure software development lifecycle practices within regulated environments.
• Ability to translate regulatory and quality requirements into practical technical controls and engineering practices.
• Ability to establish, monitor, and report on engineering quality, AI system performance, evaluation results, and operational cost.
• Ability to proactively identify technical risks, dependencies, and architectural trade offs and recommend mitigations.
• Strong analytical and problem solving skills with the ability to think strategically and operationally.
• Ability to evaluate technology trade offs, build versus buy decisions, and prioritization with incomplete information.
• Ability to manage multiple competing priorities under pressure and shifting business demands.
• Strong leadership skills with demonstrated ability to develop, coach, and mentor engineers and other team members.
• Ability to influence stakeholders and drive alignment across competing priorities without formal authority.
• Knowledge of vendor management practices including statement of work review, SLA governance, and performance evaluation.
• Strong writing skills with the ability to develop reference architectures, design documents, executive briefings, and technical standards.
• Confidence in making independent as well as collaborative decisions as required by the situation.
• Ability to work effectively in both onsite and remote environments as required by the business.