Senior AI & GenAI Solutions Architect

Senior Solutions Architect

The Senior Solutions Architect provides technical leadership and designs complex solution architectures that support business strategy and streamline technology-enabled workflows. This role partners closely with business owners, product, data, and engineering teams to document current-state systems and design scalable, resilient, and secure cloud-based solutions. This position focuses on emerging technologies, including AI, Generative AI, and machine learning, and guides solutions from research and analysis through architecture, delivery support, and operational readiness.

This job description is not intended to be an exhaustive list of all duties, responsibilities and qualifications of the job. The employer has the right to revise this job description at any time. You may be required to perform other duties that are not included on this job description.

• Lead discovery with business and technology partners to understand objectives, constraints, current-state systems, and integration points.
• Document current-state architecture and define target-state designs including system context diagrams, component designs, integration patterns, and data flows.
• Design and modernize applications into cloud-compatible or cloud-native architectures using microservices, serverless, and event-driven patterns where appropriate.
• Create strategies, roadmaps, and migration designs for transitioning applications and data workloads to cloud platforms.
• Design AI and ML-enabled solutions, including model integration into products and business processes, and patterns for scalable inference and low‑latency serving where needed.
• Design Generative AI solution patterns, such as retrieval‑augmented generation, tool and API integration, prompt and context management, and evaluation approaches.
• Define reference architectures for AI platforms and enabling capabilities, such as data pipelines, feature and embedding generation, vector storage, model endpoints, and integration with enterprise APIs.
• Establish best practices for MLOps and AI operations, including model versioning, deployment, monitoring, drift detection, incident response, and cost management.
• Incorporate security‑by‑design practices into architectures, including identity and access controls, encryption, secrets management, secure networking, and audit logging.
• Partner with governance and risk stakeholders to ensure responsible AI considerations are incorporated, including privacy, explainability, safety, compliance, and model risk controls as applicable.
• Drive alignment and adoption of proposed solutions by clearly communicating tradeoffs, risks, and value and obtaining stakeholder alignment and governance approvals. Selling means internal alignment and decision support, not external pre‑sales.
• Support teams responsible for testing and validation and help triage and resolve design‑related issues found during development, UAT, or production.
• Perform other duties as assigned.

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Business, or equivalent practical experience.
• 5+ years of experience in agile software delivery environments with increasing architecture and design responsibility.
• Demonstrated experience designing distributed systems using microservices and or serverless patterns.
• Experience designing and integrating AI and ML capabilities into applications, including model serving considerations and data dependencies.
• Experience with one or more languages such as Java, Python, Node.js, or Scala.
• Experience with data persistence technologies across SQL and No
  
  SQL.
• Experience with at least one major cloud provider, AWS, Azure, or Google Cloud, and core cloud design patterns.
• Working knowledge of CI/CD pipelines and Dev Ops practices, including automated testing and deployment automation.
• Strong communication skills and ability to translate business needs into clear technical direction.

• Hands‑on experience with GenAI and LLM solutions, including retrieval‑augmented generation, embeddings, evaluation, and production monitoring.
• Experience with AI and ML platforms or…