Practice Director | AI Solutions

JOB SUMMARY

The Practice Director, AI Solutions leads Trace3’s technical presales practice for enterprise AI. This is a client-facing solution architecture role, not a delivery role. The Practice Director owns how Trace3 frames, architects, and defends AI solutions before a statement of work exists, and builds the team of AI architects who do that work at scale.

The role sits deliberately at the intersection of three technical domains. Data Platforms builds the estate. AI Platforms provides the compute and runtime. Security and Governance set the guardrails. AI Solutions is accountable for turning all three into a coherent answer to a client business problem, and for making sure that answer survives scrutiny from a CIO, a CISO, and a CFO in the same room.

Clients are past the demo stage and into the hard part: connecting real data to real decisions, moving agentic systems from pilot into production, and proving the result is governed, secure, and worth the spend. The Practice Director is the senior technical voice Trace3 puts in front of those conversations, and the person responsible for building a team capable of having them without him or her in the room.

Architecture produces scope, and scope produces the engagement. This role is measured on the quality and commercial consequence of the architecture it puts in front of clients.

SUMMARY OF ESSENTIAL JOB FUNCTIONS

I. Technical Presales Leadership

• Serve as the senior technical authority across AI pursuits: discovery, solution architecture, technical qualification, and defense of the proposed approach through close.
• Lead an architecture-first pursuit motion in which a well-scoped design produces the statement of work, rather than retrofitting a design to a scope someone already guessed at.
• Own the technical content of AI proposals, RFP responses, and executive briefings: reference architectures, diagrams, stated assumptions, identified risks, and level of effort inputs.
• Run whiteboard sessions, technical workshops, and executive briefings with mixed audiences of engineers, architects, and business leaders.
• Partner with account executives and client partners to qualify opportunities honestly, including telling a client when AI is the wrong answer to the problem they described.
• Hand off to Services with enough documented design that delivery starts from a known architecture rather than a blank page, and remain available as technical escalation during early delivery.

II. Data for AI and Capability Alignment

• Assess client data estates for AI readiness: quality, lineage, access patterns, latency, semantics, and the governance posture required before that data can feed an AI system.
• Architect the data layer AI depends on, including retrieval and context pipelines, vector and semantic search, feature engineering for machine learning, evaluation and ground truth data sets, and the metadata that makes any of it trustworthy.
• Translate business problems into the specific data capabilities required to solve them, and say plainly where the current estate does not support the stated ambition.
• Align AI solution architecture to the client’s existing data platform investments across Databricks, Snowflake, Microsoft Fabric, and cloud-native stacks rather than proposing a parallel estate.
• Work with the Data Strategy and Data Platforms practices so that AI opportunity discovery feeds data modernization scope, and modernization work surfaces AI opportunity.

III.  Agentic AI Adoption and Implementation

• Architect agentic solutions end to end: orchestration, tool and system integration, memory and state management, human-in-the-loop controls, and evaluation.
• Establish the reference patterns Trace3 uses for agent design, model selection, context engineering, and protocol-level integration including MCP and comparable standards.
• Advise clients on the operating model agentic AI requires: ownership, change management, cost control, monitoring, and the failure modes worth designing against before they appear in production.
• Define what production readiness means for an agentic system and hold every proposed architecture to that bar.
• Build repeatable proof of concept and pilot approaches that shorten the path from client interest to funded program, and that demonstrate real integration rather than a scripted demo.

IV. Cross-Practice Technical Oversight

• Provide architectural oversight across AI opportunities regardless of which practice originates them, so Trace3 presents one coherent AI point of view to the client.
• Partner with AI Platforms on the infrastructure and runtime layer: inference architecture, sizing, on-premises versus cloud placement, and OEM alignment.
• Partner with Security and Risk on AI-specific exposure including data leakage, prompt injection, model and agent identity, third-party model risk, and alignment to NIST AI RMF and comparable frameworks.
• Coordinate with Cloud, Networking, and Applications teams where AI solutions cross practice boundaries, and establish scope ownership before the pursuit rather than after the escalation.
• Represent the Data & Analytics AI position in Center of AI, Innovation, and OCTO forums.

V. Team Building and Talent Development

• Recruit, build, and lead a team of AI architects spanning solution architecture, data for AI, and agentic engineering.
• Define the competency model, career path, and enablement plan for AI architects, and certify readiness rather than assume it.
• Mentor architects toward independent ownership of pursuits. The practice is measured in part on how many opportunities advance without the Practice Director in the room.
• Set and enforce standards for architecture artifacts, including diagram quality, stated assumptions, identified risks, and a traceable source behind every figure in client-facing material.
• Develop and deliver internal enablement so sales and delivery teams can qualify and position AI solutions accurately.

VI. Thought Leadership and Partner Ecosystem

• Serve as a public technical voice for Trace3 in AI at executive summits, client briefings, partner events, and in published perspective.
• Maintain a current working view of the model, framework, and platform landscape, including a clear read on where vendor claims outrun what the technology actually does.
• Build technical depth with strategic AI partners across hyperscalers, model providers, data platform vendors, and infrastructure OEMs, and convert that depth into differentiated client positioning.
• Feed field observation back into offering development so packaged services reflect what clients are actually buying.

SCOPE BOUNDARIES

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

• Owns: solution architecture, technical qualification, level of effort inputs, architecture standards, the AI architect team, and all presales technical content for AI opportunities.
• Does not own: delivery staffing and execution, project management, sales quota, AI infrastructure platform ownership (AI Platforms), data platform build (Data Platforms), or security policy authority (Security and Risk).
• Shared: opportunity qualification with Sales, production readiness definition with Services, and AI governance guidance with Security and Data Governance.

PERFORMANCE MEASURES

• Influenced pipeline and closed bookings on AI opportunities where the practice engaged.
• Conversion rate from advisory entry point (assessment, workshop, proof of concept) into full-stack scope.
• Quality of the delivery handoff, measured by delivery-reported rework against the presales design.
• Competitive technical win rate on contested AI pursuits.
• Team capability growth, measured by the number of architects leading pursuits independently.

REQUIRED SKILLS AND EXPERIENCE

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field. Advanced degree preferred, though demonstrated depth carries more weight than credentials.
• 10+ years in technology, including 5+ years architecting and advising on AI, machine learning, or data solutions in a client-facing capacity.
• 3+ years in technical presales, solutions architecture, or consulting with direct responsibility for winning the technical decision.
• Experience leading or mentoring technical teams. Formal people leadership experience preferred.
• Hands-on technical depth in Python, modern AI and ML frameworks, LLM application development, retrieval architectures, and agentic orchestration frameworks. This role reviews code and architecture, not only slides.
• Working command of at least two major cloud AI stacks (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 the operational realities of running AI systems in production.
• Command of AI security and governance including NIST AI RMF, data protection, model risk, and the controls enterprises actually require before approving production use.
• Proven ability to lead executive-level technical conversations, including the credibility to disagree with a client and keep the relationship.
• Excellent written and verbal communication, with the range to move between a CISO, a data engineering team, and a CFO in the same day.
• 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.
• Published technical writing, conference speaking, or a visible body of applied AI work.