Sr. Solutions Architect, AI Solutions

Description

The company is a technology solutions and consulting firm that helps IT executives gain a competitive edge through integration, automation and innovation. It has the feel of a startup with the stability of a scalable business, and it employs more than 1,200 people across the United States.

The company is headquartered in Irvine, California, with field offices in Denver, Indianapolis, Grand Rapids, Lexington, Los Angeles, Louisville, Texas and San Francisco.

This is a hands-on technical presales role in the Data and Analytics AI Solutions practice, reporting to the Practice Director, AI Solutions. You design and defend AI solutions before a statement of work exists. The role does not include delivery execution.

The focus is AI built on data: linking a client's data estate to real business decisions and moving agentic workflows from pilot into production. You run discovery, design the architecture, build working prototypes when a pursuit needs them, and write the technical content that becomes the scope. Designs draw on data platforms, AI platforms for compute and runtime, and security for guardrails, and must hold up with both engineers and executives.

You are measured on the quality of your architectures and their business results: pursuits won, scope delivery can execute without rework, and advisory work that grows into full-stack programs.

Responsibilities

• Own the technical solution on assigned AI pursuits, from qualification and design through to defending 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 fix.
• 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 architectures to technical and executive audiences, including CIO, CDO and CISO stakeholders.
• Work with account executives to qualify opportunities honestly, and tell clients when AI is the wrong answer.
• Hand off a documented design to Services so delivery starts from a known architecture, and support technical escalations in early delivery.
• Assess client data estates for AI readiness across quality, lineage, access patterns, latency, semantics and governance.
• Design the data layer AI relies on: retrieval and context pipelines, vector and semantic search, feature engineering, evaluation and ground truth datasets, and metadata.
• Fit AI architectures to the client's existing investments in Databricks, Snowflake, Microsoft Fabric or cloud-native stacks.
• State clearly when a client's data cannot support the AI goal, and define the data work needed to close the gap.
• Work with Data Strategy and Data Platforms architects so AI discovery shapes data modernization scope.
• 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 connect to real client data and systems and 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.
• Work with AI Platforms architects on inference architecture, sizing and on-premises versus cloud placement.
• Work with Security and Risk on AI-specific exposure such as 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 with the Practice Director, AI Solutions early in the pursuit.
• Contribute reusable architecture patterns, demo assets and discovery guides to the practice after each pursuit.
• Meet the practice's standards for architecture artifacts, including diagram quality, stated assumptions and a traceable source for 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.
• Keep hands-on knowledge of models, agent frameworks and AI and data platforms, including where vendor claims go beyond what the technology does today.
• Build technical relationships with partner solution architects at hyperscalers, model providers and data platform vendors.
• Support thought leadership through client briefings, partner events, executive summits and written perspectives.
• This role does not perform delivery work, carry billable utilization or own project P&L, and it does not handle delivery staffing, project management, sales quota, practice standards or team leadership, AI infrastructure, data platform build, or security policy. Opportunity qualification with Sales and the delivery handoff with Services are shared.

Requirements

• 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 skill in Python, modern AI and ML frameworks, LLM application development, retrieval architectures and agent orchestration frameworks. You build working prototypes as well as slides.
• Working experience with at least one major cloud AI stack (Azure AI Foundry, AWS Bedrock or Google Vertex AI) and at least one modern data platform (Databricks, Snowflake or 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 need before approving production use.
• Confidence to lead technical conversations with executives and engineers, including disagreeing with a client while keeping the relationship.
• Strong written and verbal communication, able to explain AI architecture to technical and non-technical audiences.
• Highly organized, able to run several concurrent pursuits in a fast-paced setting.
• Willing to travel roughly 30 to 50 percent, depending on region and pursuit load.
• Preferred: experience in a consulting, advisory or solutions firm where technical design drives commercial scope.
• Preferred: certifications in cloud AI, data platforms or ML engineering.
• Preferred: industry depth in healthcare, financial services, public sector or manufacturing.
• Preferred: a visible body of applied AI work, such as published writing, open source contributions or conference talks.

Conditions

Estimated pay range is $190,000 - $200,000 USD.

Actual salary depends on location, experience, skill set, performance, licensure and certification, and business needs. Pay in other locations may differ.

The role may be eligible for variable incentive pay, such as bonuses or commissions, which is not included in base salary.

The company is an equal opportunity employer and makes employment decisions based on qualifications, merit and business needs, without discrimination on protected characteristics.

Benefits

• Comprehensive medical, dental and vision plans for you and your dependents.
• 401(k) with employer match, 529 college savings plan, health savings account, life insurance and long-term disability.
• Competitive compensation.
• Training and development programs.
• Snacks and beverages in major offices.
• A collaborative team culture.
• Work-life balance and generous paid time off.