Solutions Architect (AI)

This individual will serve as the technical leader across client engagements, translating complex business challenges into scalable architecture and production-ready solutions.

The ideal candidate will have a background in Data Science, Data Architecture, or Enterprise Data Platforms and strong client-facing experience. This is a highly hands-on role requiring the ability to design architecture, guide implementation, and work directly with C-Suite stakeholders., * 5+ years of experience in a Solution Architect, Data Architect, Principal Data Engineer, Analytics Architect, Machine Learning Engineer, Technical Consultant, Implementation Architect, or Technical Delivery role. \n

• Strong background in Data Architecture, Data Science, , Data Analytics, Data Platforms, or Enterprise Data Engineering. \n

• Hands-on experience with Python, SQL, APIs, cloud platforms (AWS, Azure, or GCP), and modern data ecosystems. \n

• Experience designing and deploying production-grade AI, analytics, machine learning, automation, or data-driven applications. \n

• Proven ability to engage with executive stakeholders while translating business requirements into scalable technical solutions. \n

• Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or a related quantitative field. Advanced degrees are highly preferred. \n

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• Lead the technical design and delivery of enterprise AI, analytics, and data platform solutions from discovery through deployment and operational handoff. \n

• Partner directly with business, data, analytics, engineering, security, and IT teams to gather requirements and define solution architecture. \n

• Design scalable integrations across APIs, data platforms, identity systems, workflow tools, cloud infrastructure, and enterprise applications. \n

• Translate ambiguous business problems into technical roadmaps, implementation plans, architecture diagrams, and measurable outcomes. \n

• Develop proof of concepts, data workflows, AI solutions, automations, and production-ready implementations. \n

• Establish standards around security, governance, reliability, observability, testing, and deployment within enterprise environments. \n

• Communicate technical risks, architecture decisions, and implementation tradeoffs to both executive and technical audiences. \n

• Drive adoption, user enablement, documentation, operational readiness, and successful production rollout. \n

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