Sr. Solutions Architect, AI Solutions

JOB SUMMARY:

The Sr. Solutions Architect, AI Solutions is a hands-on technical presales role in Trace3’s Data & Analytics AI Solutions practice. Reporting to the Practice Director, AI Solutions, the Sr. Solutions Architect designs and defends the AI solutions Trace3 puts in front of clients before a statement of work exists. The role is presales only and does not carry delivery execution.

The work centers on AI from a data and analytics perspective: connecting a client’s data estate to real business decisions, and moving agentic workflows from pilot into production. The Sr. Solutions Architect runs discovery, designs the architecture, proves it with working prototypes when a pursuit calls for one, and writes the technical content that becomes the scope. Each design draws on Data Platforms for the estate, AI Platforms for compute and runtime, and Security for the guardrails, and has to hold up in front of engineers and executives alike.

The role is measured on the quality of the architectures it produces and on their commercial result: pursuits won, scope that delivery can execute without rework, and advisory work that grows into full-stack programs.

SUMMARY OF ESSENTIAL JOB FUNCTIONS:

1. Technical Presales and Solution Architecture

• Own the technical solution on assigned AI pursuits, from discovery and qualification through solution design and defense of the approach at close.
• Run discovery sessions, whiteboards, and technical workshops with client engineers, architects, and business stakeholders to define the problem before proposing a solution.
• Produce reference architectures, solution diagrams, stated assumptions, identified risks, and level of effort inputs for proposals, RFP responses, and statements of work.
• Present and defend proposed architectures to technical and executive audiences, including CIO, CDO, and CISO stakeholders.
• Partner with account executives to qualify opportunities honestly, including advising a client when AI is the wrong answer to the problem described.
• Hand off to Services with documented design so delivery starts from a known architecture, and support technical escalation during early delivery.

1. Data for AI

• Assess client data estates for AI readiness: quality, lineage, access patterns, latency, semantics, and governance posture.
• Design the data layer AI depends on, including retrieval and context pipelines, vector and semantic search, feature engineering, evaluation and ground truth data sets, and supporting metadata.
• Align AI architectures to the client’s existing data platform investments across Databricks, Snowflake, Microsoft Fabric, and cloud-native stacks.
• State plainly where a client’s data estate cannot support the stated AI goal, and define the data work required to close the gap.
• Work with Data Strategy and Data Platforms architects so AI discovery informs data modernization scope.

• Agentic AI Solutions

• Design agentic workflows end to end: orchestration, tool and system integration, memory and state, human-in-the-loop controls, and evaluation.
• Apply the practice’s reference patterns for agent design, model selection, context engineering, and protocol-level integration including MCP, and recommend improvements based on field results.
• Build proofs of concept and pilots that demonstrate real integration with client data and systems, scoped to show a credible path to production.
• Test proposed agentic solutions against the practice’s production readiness standard for security, monitoring, cost, and failure handling.
• Advise clients on the operating model changes agentic AI requires, including ownership, change management, and cost control.

1. Cross-Practice Collaboration

• Engage AI Platforms architects on inference architecture, sizing, and on-premises versus cloud placement for assigned pursuits.
• Engage Security and Risk on AI-specific exposure, including data leakage, prompt injection, agent identity and access, and alignment to NIST AI RMF.
• Coordinate with Cloud, Applications, and Customer Experiences teams when a solution crosses practice boundaries, and confirm scope ownership before the proposal goes out.
• Raise architecture questions and scope conflicts to the Practice Director, AI Solutions early in the pursuit.

1. Enablement and Knowledge Sharing

• Contribute reusable architecture patterns, demo assets, and discovery guides back to the practice after each pursuit.
• Meet the practice’s standards for architecture artifacts, including diagram quality, stated assumptions, and a traceable source behind every figure in client-facing material.
• Deliver enablement to sales and account teams so they can qualify and position AI solutions accurately.
• Mentor less experienced architects and engineers through pursuit work and peer review.

1. Thought Leadership and Partner Depth

• Keep current, hands-on knowledge of models, agent frameworks, and AI and data platforms, including where vendor claims exceed what the technology delivers today.
• Build technical relationships with partner solution architects across hyperscalers, model providers, and data platform vendors.
• Support Trace3 thought leadership through client briefings, partner events, executive summits, and written perspective.

SCOPE BOUNDARIES:

This role is presales. It is accountable for the technical solution before it is sold and for the quality of the handoff into delivery. It does not perform delivery work, carry billable utilization, or own project P&L.

• Owns: solution architecture, technical qualification, level of effort inputs, proofs of concept, and presales technical content on assigned AI pursuits.
• Does not own: delivery staffing and execution, project management, sales quota, practice standards and team leadership (Practice Director, AI Solutions), AI infrastructure platforms (AI Platforms), data platform build (Data Platforms), or security policy (Security and Risk).
• Shared: opportunity qualification with Sales, and the delivery handoff with Services.

PERFORMANCE MEASURES:

• Technical win rate on assigned AI pursuits, including contested competitive pursuits.
• Influenced pipeline and closed bookings on pursuits where the role led the technical solution.
• Conversion from advisory entry points (assessment, workshop, proof of concept) into full-stack scope.
• Quality of the delivery handoff, measured by delivery-reported rework against the presales design.
• Reusable assets and patterns contributed back to the practice.

REQUIRED SKILLS AND EXPERIENCE:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent experience.
• 7+ years in technology, including 4+ years designing or building AI, machine learning, or data solutions.
• 2+ years in technical presales, solutions architecture, or consulting with responsibility for the technical decision on client pursuits.
• Hands-on proficiency in Python, modern AI and ML frameworks, LLM application development, retrieval architectures, and agent orchestration frameworks. This role builds working prototypes as well as slides.
• Working experience with at least one major cloud AI stack (Azure AI Foundry, AWS Bedrock, Google Vertex AI) and at least one modern data platform (Databricks, Snowflake, Microsoft Fabric).
• Practical experience with model evaluation, cost and performance tradeoffs, and running AI systems in production.
• Working knowledge of AI security and governance, including NIST AI RMF, data protection, and the controls enterprises require before approving production use.
• Ability to lead technical conversations with executives and engineers, including the confidence to disagree with a client and keep the relationship.
• Excellent written and verbal communication, with the ability to explain AI architecture to technical and non-technical audiences.
• Highly organized and able to run several concurrent pursuits in a fast-paced environment.
• Willingness to travel, approximately 30 to 50 percent depending on region and pursuit load.

PREFERRED QUALIFICATIONS:

• Experience in a consulting, advisory, or solutions firm where technical design drives commercial scope.
• Certifications across cloud AI, data platform, or ML engineering disciplines.
• Industry depth in one or more of healthcare, financial services, public sector, or manufacturing.
• A visible body of applied AI work, such as published writing, open source contributions, or conference talks.