Salary: $200K plus BonusNOTE: MUST have previous experience working for an Enterprise VAR, Systems Integrator, or major technology Manufacturer.***NO RECRUITING AGENCIES*** ***NO C2C*** ***NO Sponsorship available*** Role SummaryPrincipal Solutions Architect - AI, Applications & DevOps is a customer-facing technical leader embedded within the sales account team, responsible for defining solution vision, shaping technical strategy, and architecting modern application and Generative AI solutions that drive measurable business outcomes. This role bridges customer business objectives with secure, scalable, and practical architectures-spanning application platforms, data, identity, and GenAI capabilities.
Architectural Oversight: This individual will also provide architectural oversight on existing customer engagements, working with Delivery leadership to ensure what we sell aligns with what we deliver and that we're achieving the customer's expected outcomes.
This individual partners closely with Account Executives, customer stakeholders, and internal delivery teams to lead discovery, design end-to-end solutions, and guide opportunities from early technical validation through proposal development and transition to delivery. The role requires equal strength in executive-level solution storytelling, deep architectural design, and hands-on technical validation through prototypes and demonstrations.
This position is suited for a seasoned technologist with experience in consulting, solutions architecture, or technical pre-sales, where customer-facing architecture leadership, complex solution design, and outcome-driven engagements were core responsibilities.
Core Responsibilities• Pre-Sales Engineering & Solution Architecture
• Lead technical discovery to understand customer business objectives, operating models, application landscapes, data maturity, security posture, and adoption constraints.
• Architect end-to-end solutions across frontend applications, APIs, microservices, data platforms, identity, observability, and Generative AI components.
• Design Generative AI architectures as applicable, including Retrieval-Augmented Generation (RAG), agentic workflows, tool/function calling, orchestration patterns, evaluation frameworks, and LLMOps practices.
• Develop high-quality customer-facing deliverables, including solution narratives, architecture diagrams, implementation approaches, estimates, and risk/assumption documentation.
• Guide scoping inputs for Statements of Work (SOWs), including scope definition, milestones, acceptance criteria, and implementation considerations, ensuring proposed solutions are viable and deliverable.
• Clearly articulate architectural tradeoffs (build vs. buy, model selection, data strategy, latency and cost considerations, security and compliance impacts) and recommend pragmatic paths forward.
Customer Engagement & Technical Evangelism• Serve as a trusted advisor and primary technical interface for assigned accounts.
• Lead workshops, whiteboarding sessions, and technical deep dives with business and technical stakeholders.
• Translate business outcomes into compelling technical solution visions and roadmaps ("north star" architectures).
• Build trusted relationships with customer leadership, including CTO/CIO, VP Engineering, Security leaders, and Data/AI stakeholders.
• Communicate complex application and AI concepts clearly to both executive and engineering audiences.
Demonstrations, Prototypes, and Proof of Value• Lead or guide the development of rapid prototypes and proof-of-concept engagements to validate feasibility and accelerate decision-making.
• Deliver demonstrations that clearly communicate time-to-value and measurable business impact.
• Define and track success metrics for pilots and POCs, including accuracy, grounding quality, hallucination controls, latency, cost, and user adoption.
• Present outcomes and recommendations to executive and technical stakeholders.
Governance, Security, and Responsible AI• Ensure solution designs meet enterprise requirements for security, privacy, reliability, and compliance, including identity, encryption, auditability, and data retention.
• Establish responsible AI guardrails, including data handling practices, safety and toxicity controls, human-in-the-loop patterns, evaluation, monitoring, and model risk considerations.
• Collaborate with customer security, legal, and compliance stakeholders to align solution designs with internal policies and regulatory requirements.
Cross-Functional Collaboration & Internal Enablement• Partner with Account Executives to support opportunity qualification, pipeline creation, and deal progression.
• Collaborate with delivery leads, architects, and engineering teams to ensure smooth transition from pre-sales to implementation with minimal rework.
• Develop reusable reference architectures, accelerators, and best-practice patterns for application and GenAI delivery.
• Mentor peers across sales and delivery teams on solution patterns, common pitfalls, and effective customer messaging.
• Provide field feedback to product and engineering teams to influence roadmap and service offerings.
Requirements
Technical & Professional Background10+ years of progressive IT experience, including application architecture and customer-facing technical leadership.
5+ years in consulting, solutions architecture, sales engineering, or technical pre-sales roles supporting complex enterprise accounts.
5+ years designing and delivering cloud-based solutions, including architecture, security, operations, and cost considerations.
Strong full-stack application architecture experience, including:
- Modern web application patterns (SPA/SSR) and API design
- Microservices and event-driven architectures
- Data platforms (SQL and NoSQL), caching, and integration patterns
- Observability, reliability, and performance engineering
Hands-on ability to build or guide delivery teams using modern frameworks (e.g., React/Next.js with TypeScript) and modern DevOps practices (CI/CD, infrastructure-as-code concepts, containerization).
Demonstrated experience delivering Generative AI solutions in production, pilot, or advanced prototype contexts, including several of:
- Retrieval-Augmented Generation (RAG), embeddings, vector databases
- Document ingestion and processing pipelines
- Prompt engineering and structured output patterns
- Model evaluation, monitoring, and LLMOps practices
- Agentic workflows and tool integration patterns
Key Skills and Competencies- VAR / Manufacturer Background: Strong preference for someone who has worked at an enterprise VAR, systems integrator, or major technology manufacturer such as Microsoft, AWS, Cisco, or Google.
- Services Sales Experience: Proven ability to partner with Account Executives, develop pipeline, influence customer buying decisions, and help close professional services opportunities.
- Technical Depth: Strong architecture background across AI/GenAI, application development and modernization, DevOps, and cloud-native technologies.
- Customer Success & Outcomes: Someone who leads with business value, understands customer challenges, and can build trusted relationships with executive stakeholders.
Technical & Architectural Skills• Ability to design secure, scalable, and maintainable application and GenAI architectures.
• Strong understanding of data strategy, identity integration, and enterprise application ecosystems.
• Capability to map business goals to technical architectures and delivery approaches.
• Consultative & Pre-Sales Skills
• Exceptional whiteboarding, solution storytelling, and executive communication skills.
• Ability to create persuasive technical proposals, estimates, and architecture documentation.
• Strong discovery skills to uncover both explicit and implicit business and technical requirements.
• Comfort operating in fast-paced, high-volume sales environments with shifting priorities.
Preferred QualificationsCloud architecture certifications (vendor-neutral or vendor-specific).
Experience with enterprise security and compliance requirements (e.g., SOC 2, HIPAA, PCI, GDPR).
Experience integrating Generative AI capabilities into enterprise systems such as CRM, ERP, ticketing platforms, knowledge bases, and data warehouses.
Familiarity with common AI platforms, vector stores, orchestration frameworks, and evaluation toolchains.
Proven track record of developing reusable reference architectures, accelerators, or technical enablement content.
Success Criteria (KPIs)Increased technical win rate and reduced sales cycle duration through strong solution vision and technical validation.
High customer confidence driven by executive-ready communication and credible engineering depth.
Minimal rework during transition from pre-sales to delivery due to clear scope, architecture, and risk management.
Repeatable architectures and assets that improve proposal quality, delivery consistency, and time-to-value.
Travel may be required based on customer and account needs. 
