Job DescriptionOTSI (Object Technology Solutions, Inc) has an immediate opening for an
AI & Data Solutions ArchitectLocation: 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 salescycles. Partner with Sales to shape deal strategy, facilitate architecturedesign sessions with C-suite stakeholders, define solution scope, andbuild compelling business and technical narratives.
- End-to-End ArchitectureDesign: Architect scalable, cloud-native software solutions and modern dataplatforms (e.g. Microsoft Fabric, Databricks, Snowflake) aligned withenterprise analytics and AI initiatives.
- Delivery Oversight &Hands-On Execution: Provide technical leadership to global development and data engineeringteams. Serve as the definitive technical escalation point who canconfigure systems, develop scripts, or build proofs-of-concept to ensurethe delivery of critical project milestones.
- Advanced AI Strategy: Design robust AI/MLsolutions that advance beyond foundational LLM integrations. Guide clientsin implementing Agentic AI workflows, autonomous orchestration, and secureenterprise integrations utilizing frameworks such as the Model ContextProtocol (MCP).
- Governance &Optimization: Ensure architectural consistency, quality, and strict adherence toenterprise AI governance and security frameworks throughout the SDLC.Optimize cloud architectures across Azure, AWS, and GCP to balanceinnovation, performance, and cost efficiency.
- Research &Development: Stay up to date with AI/ML technologies, advancements, and trends. Provideinsights to guide internal R&D efforts on company products, tools, andaccelerators outside of client engagements.
Required Skills: Consulting, Presales & Leadership- Client Engagement: 8+ years inclient-facing presales, consulting, or solution architecture roles. Provenability to facilitate executive discussions, translate complex technicalconcepts into clear business value, and drive consensus among enterprisestakeholders.
- Executive Presentation: Exceptional white boarding and communication skills. Demonstrated capability todynamically design and articulate modern data architectures for bothengineering leadership and business executives.
- Global Collaboration: Experience mentoringdevelopment teams and partnering seamlessly across a global delivery modelto ensure the successful hand off, translation, and execution of definedarchitectures.
Required Skills: Core Technical Expertise- Cloud & DataPlatforms: 7+ years designing cloud-native architectures (Azure, AWS, or GCP). Deeparchitectural knowledge of modern data platforms (preferably Databricks orMicrosoft Fabric) and distributed compute frameworks (Apache Spark).
- Applied AI & MachineLearning: Strongarchitectural experience designing AI/ML solutions, vector databases, andRAG architectures. Expertise in developing Agentic AI systems and workflowautomation utilizing frameworks such as LangChain and the Model ContextProtocol (MCP).
- Practitioner Capability: Retained hands-onengineering proficiency with a strong command of Python and SQL,alongside experience in highly scalable backend languages like Java orGo. Fully capable of executing detailed technical work and navigatingthe 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, scalabledeployment 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).
