Sr. Solutions Architect, AI Solutions

Trace3, United States +1

The role

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.

Posted pay

$190K–$200K

a year

Compared with similar jobs

This base pay is near the median for senior solutions engineering jobs in other US places.

Median: $188K a year
Other jobs in the group: 239

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location

United States +1

Work type

Not given

Level

Senior

Posted

14 days ago

Last check

12 min ago

Job type

Solutions engineering

The role

Summary of essential job functions

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.
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.
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.
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.
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.

Sr. Solutions Architect, AI Solutions

The role

Sr. Solutions Architect, AI Solutions

The role

What you need

Nice to have

Street-Smart - Thriving in Dynamic Times

Teamwork - Humble, Hungry and Smart

Summary of essential job functions

Scope boundaries

Performance measures

Estimated Pay Range

The Perks

About Trace3

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