AI & Data Solutions Architect

About the role

OTSI (Object Technology Solutions, Inc) has an immediate opening for an AI & Data Solutions Architect Location: Seattle (Remote, some travel required) We are seeking a highly technical, client-facing AI & Data Solutions Architect to lead enterprise engagements, drive presales strategy, and facilitate architecture design sessions. You will serve as a trusted advisor to our clients, architecture complex data modernization and AI adoption strategies. While this role heavily emphasizes client interaction, executive presentation, and architectural design, it requires a strong technical practitioner who is fully capable of engaging in hands-on development and technical problem-solving to support global delivery teams and ensure project success. Key Responsibilities Strategic Presales & Solution Architecture: Act as the lead technical strategist during sales cycles. Partner with Sales to shape deal strategy, facilitate architecture design sessions with C-suite stakeholders, define solution scope, and build compelling business and technical narratives. End-to-End Architecture Design: Architect scalable, cloud-native software solutions and modern data platforms (e.g. Microsoft Fabric, Databricks, Snowflake) aligned with enterprise analytics and AI initiatives. Delivery Oversight & Hands-On Execution: Provide technical leadership to global development and data engineering teams. Serve as the definitive technical escalation point who can configure systems, develop scripts, or build proofs-of-concept to ensure the delivery of critical project milestones. Advanced AI Strategy: Design robust AI/ML solutions that advance beyond foundational LLM integrations. Guide clients in implementing Agentic AI workflows, autonomous orchestration, and secure enterprise integrations utilizing frameworks such as the Model Context Protocol (MCP). Governance & Optimization: Ensure architectural consistency, quality, and strict adherence to enterprise AI governance and security frameworks throughout the SDLC. Optimize cloud architectures across Azure, AWS, and GCP to balance innovation, performance, and cost efficiency. Research & Development: Stay up to date with AI/ML technologies, advancements, and trends. Provide insights to guide internal R&D efforts on company products, tools, and accelerators outside of client engagements. Required Skills: Consulting, Presales & Leadership Client Engagement: 8+ years in client-facing presales, consulting, or solution architecture roles. Proven ability to facilitate executive discussions, translate complex technical concepts into clear business value, and drive consensus among enterprise stakeholders. Executive Presentation: Exceptional white boarding and communication skills. Demonstrated capability to dynamically design and articulate modern data architectures for both engineering leadership and business executives. Global Collaboration: Experience mentoring development teams and partnering seamlessly across a global delivery model to ensure the successful hand off, translation, and execution of defined architectures. Required Skills: Core Technical Expertise Cloud & Data Platforms: 7+ years designing cloud-native architectures (Azure, AWS, or GCP). Deep architectural knowledge of modern data platforms (preferably Databricks or Microsoft Fabric) and distributed compute frameworks (Apache Spark). Applied AI & Machine Learning: Strong architectural experience designing AI/ML solutions, vector databases, and RAG architectures. Expertise in developing Agentic AI systems and workflow automation utilizing frameworks such as LangChain and the Model Context Protocol (MCP). Practitioner Capability: Retained hands-on engineering proficiency with a strong command of Python and SQL, alongside experience in highly scalable backend languages like Java or Go. Fully capable of executing detailed technical work and navigating the modern SDLC. AI Productivity & Infrastructure: Active utilization of AI productivity tools (e.g., GitHub Copilot, Claude) to accelerate development. Solid understanding of containerization (Docker, Kubernetes) and CI/CD pipelines to ensure the reliable, scalable deployment of AI models into production environments. Enterprise Integration: Expertise in designing robust data pipelines, semantic models, and API integrations that seamlessly connect AI capabilities within complex, legacy enterprise environments (e.g., SAP, Oracle).