AI Solutions Architect

Accountabilities:

AI Solution Design and Development: Design, develop, operationalize, and deploy AI, machine learning, and large language model (LLM)-based solutions in production environments. Build end-to-end applications using Python, APIs, cloud services, and modern AI frameworks.
Enterprise AI Architecture: Define and implement scalable AI architecture patterns covering data ingestion, model integration, model tuning, orchestration, and application layers. Develop solutions incorporating retrieval-augmented generation (RAG), vector databases, semantic layers, AI assistants, and orchestration frameworks.
Data Engineering and Integration: Design, build, and optimize data pipelines and integrations that support enterprise AI use cases. Ensure reliable, efficient, and secure data flows across business systems and operational environments.
Manufacturing Data Integration: Partner with operational technology (OT), IT, and data engineering teams to integrate manufacturing and industrial data. Develop solutions supporting predictive maintenance, quality monitoring, operational performance, and process optimization.
Enterprise Copilots and AI Enablement: Lead the implementation of enterprise copilots, including Microsoft Copilot and custom GPT-based applications. Connect AI solutions securely to internal data sources and identify opportunities to deploy AI capabilities across different business functions.
Technical Leadership and Standards: Establish reusable architecture frameworks, engineering standards, and best practices. Conduct technical design reviews, guide engineering teams, and support the successful implementation of complex AI initiatives.
Business Partnership and AI Adoption: Collaborate with business stakeholders to understand operational challenges, define requirements, assess potential value, and establish success criteria. Drive AI adoption through improvements in people, processes, systems, and data.
AI Governance and Compliance: Advise and support the internal AI governance committee, establishing guidelines for responsible AI adoption, model governance, data protection, and information security. Ensure solutions comply with enterprise policies and applicable regulatory requirements.
Security and Risk Management: Incorporate appropriate security controls, identity management, authentication, and role-based access controls into AI architectures to protect sensitive information and ensure responsible technology deployment.
Requirements:
Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related discipline, or equivalent practical experience.
At least 5 years of professional experience in software engineering, data engineering, solution architecture, or a closely related technical field.
Demonstrated hands-on experience designing, developing, and deploying AI/ML solutions and large language model applications.
Strong programming skills, particularly in Python, along with experience in API development, integrations, and software engineering.
Solid understanding of cloud platforms, modern data architectures, system design, and enterprise technology environments.
Proven ability to independently design and implement complex technical solutions, including scalable AI architectures and reliable data pipelines.
Experience defining architectural patterns, establishing technical standards, conducting design reviews, and providing guidance to engineering teams.
Strong knowledge of modern AI technologies and concepts, including machine learning, LLMs, retrieval-augmented generation (RAG), vector databases, semantic layers, and orchestration frameworks.
Understanding of responsible AI principles, data protection, cybersecurity, model governance, and regulatory compliance requirements.
Strong analytical and problem-solving skills, with the ability to translate complex business challenges into practical and scalable technical solutions.
Excellent communication and collaboration skills, with the ability to work effectively with technical teams, business stakeholders, and senior leadership.
Ability to manage multiple initiatives, exercise sound technical judgment, and balance innovation with security, scalability, and operational reliability.
Preferred Qualifications:
Previous experience in manufacturing, industrial, or other operational technology environments, particularly involving IT/OT integrations.
Familiarity with Microsoft Copilot, Azure OpenAI, or comparable enterprise AI platforms.
Experience working with Azure cloud services, modern AI/ML frameworks, and Python-based development environments.
Knowledge of data lakes, real-time streaming architectures, and large-scale telemetry data processing.
Experience implementing RAG architectures, vector databases, semantic search, and AI orchestration frameworks.
Familiarity with identity and access management, authentication mechanisms, and role-based access control (RBAC).
Understanding of NIST or comparable cybersecurity, governance, and compliance frameworks.
Benefits:
Competitive compensation: Salary and compensation details are not specified in the available job description.
Remote flexibility: Fully remote working arrangement within the United States.
Full-time opportunity: Permanent full-time position with the opportunity to contribute to strategic enterprise AI initiatives.
Professional development: Opportunities to expand technical expertise and work with advanced AI, machine learning, cloud, and data engineering technologies.
Global exposure: Opportunity to collaborate with international teams and contribute to technology initiatives supporting a global industrial business.
Innovation-driven environment: Work on impactful projects involving enterprise AI adoption, intelligent automation, and manufacturing optimization.
Inclusive workplace: Equal employment opportunities and a professional environment that values diversity, respect, and individual contributions.
Veteran support: Military veterans are encouraged to apply, with transferable skills and experience recognized.