AI Product Lead / Solution Architect

Varmoda is seeking an experienced AI Product Lead / Solution Architect to lead the discovery, architecture, and accelerated delivery of an AI-enabled enterprise intake and workflow automation platform. This role will combine product leadership, solution architecture, human-centered design, and AI-native software engineering to deliver a production-ready Minimum Viable Product within a defined pilot period.

The ideal candidate will have extensive experience leading enterprise application delivery, digital transformation, and AI-enabled product initiatives. This individual will facilitate discovery, define the product roadmap, establish the solution architecture, manage an Agile backlog, guide AI-assisted development, and coordinate stakeholders throughout the product lifecycle.

Key Responsibilities

• Lead discovery workshops, stakeholder interviews, user research, and current-state assessments.
• Analyze enterprise software, infrastructure, service, and technology-request processes to identify user needs, operational challenges, and automation opportunities.
• Define the product vision, MVP scope, business outcomes, and implementation priorities.
• Translate business needs into product capabilities, epics, features, user stories, and measurable acceptance criteria.
• Develop user personas, journey maps, process maps, and problem statements.
• Apply Human-Centered Design and Design Thinking methods to improve usability and adoption.
• Validate assumptions through stakeholder feedback, prototypes, demonstrations, and iterative testing.
• Maintain alignment among business objectives, user needs, technical feasibility, security requirements, and delivery constraints.
• Product Leadership and Roadmap Management
• Own the product backlog, roadmap, release plan, and sprint priorities.
• Lead backlog refinement, sprint planning, release planning, demonstrations, and product reviews.
• Establish a structured prioritization framework using Reach, Impact, Confidence, and Effort scoring.
• Evaluate competing requests and recommend priorities based on value, urgency, complexity, risk, dependency, and user impact.
• Define MVP boundaries and manage scope throughout the pilot.
• Develop release objectives, milestones, dependencies, and readiness criteria.
• Communicate product status, risks, decisions, roadmap changes, and delivery outcomes to stakeholders.
• Maintain traceability among business objectives, priorities, user stories, architecture decisions, and delivered capabilities.
• Solution Architecture
• Define the end-to-end solution architecture for an AI-enabled enterprise intake and workflow platform.
• Develop the integration strategy across cloud services, workflow platforms, DevOps tools, APIs, data sources, and enterprise applications.
• Design scalable, maintainable, secure, and extensible architecture patterns.
• Define architectural boundaries, logical components, interfaces, data flows, security controls, and deployment considerations.
• Evaluate build, buy, configure, and integration options.
• Establish standards for application integration, API management, authentication, authorization, logging, monitoring, and error handling.
• Lead architecture reviews and document key technical decisions, assumptions, risks, and dependencies.
• Ensure the architecture supports future expansion across additional operational processes and business functions.
• Define practical use cases for generative AI, large language models, conversational AI, AI agents, and retrieval-augmented generation.
• Guide the design of AI-assisted recommendation, classification, routing, summarization, risk-assessment, and decision-support capabilities.
• Establish approaches for prompt engineering, context management, source grounding, human review, and output validation.
• Evaluate AI platforms and models based on business value, accuracy, security, cost, integration requirements, and operational supportability.
• Define controls for the responsible use of AI-generated content and recommendations.
• Incorporate human oversight into workflows involving material business, policy, security, or operational decisions.
• Collaborate with technical teams to define testing and monitoring approaches for AI-assisted capabilities.
• Identify and document known limitations, assumptions, dependencies, and risks associated with AI-enabled features.
• Coordinate AI-enabled activities across the software development lifecycle.
• Apply AI-assisted techniques to requirements analysis, user-story development, architecture documentation, code generation, testing, debugging, and technical writing.
• Establish standards for the responsible use of AI coding assistants and generative AI tools.
• Define review and approval expectations for AI-generated requirements, code, test cases, and documentation.
• Guide development teams in using AI tools to improve delivery speed without compromising quality, security, or maintainability.
• Promote reusable prompts, development patterns, testing methods, and knowledge assets.
• Measure the effect of AI-assisted development on productivity, quality, rework, and delivery outcomes.
• Develop recommendations for expanding successful AI-assisted delivery practices.
• Design enterprise intake processes for software, infrastructure, service, and technology requests.
• Define request categories, required information, business rules, decision points, service pathways, and approval requirements.
• Establish automated routing, prioritization, assignment, notification, and escalation workflows.
• Define workflows for request review, technical assessment, risk evaluation, approval, implementation, and closure.
• Configure or guide the implementation of case management, request management, and service delivery capabilities.
• Reduce manual handoffs, duplicate data entry, fragmented communication, and unclear ownership.
• Establish governance for workflow configuration, rule changes, approvals, exceptions, and continuous improvement.
• Ensure intake processes provide clear status visibility and an understandable user experience.
• Lead Agile, Scrum, Lean, or product-oriented delivery activities in an accelerated environment.
• Coordinate work across product, architecture, development, testing, DevOps, security, and business teams.
• Define sprint goals, delivery increments, release criteria, and production-readiness expectations.
• Track delivery velocity, blockers, dependencies, scope changes, defects, change failures, and quality indicators.
• Facilitate daily coordination, sprint reviews, retrospectives, risk discussions, and decision-making sessions.
• Resolve delivery obstacles and escalate decisions when necessary.
• Maintain focus on shipping a usable, supportable, and production-ready MVP within the established pilot window.
• Balance speed with security, quality, usability, operational readiness, and long-term maintainability.
• Guide implementation of operational monitoring and consolidated management dashboards.
• Define metrics covering intake volume, processing time, backlog health, request status, approvals, delivery velocity, quality, adoption, and satisfaction.
• Develop success measures that demonstrate business value and pilot effectiveness.
• Define data sources, calculation logic, reporting frequency, ownership, and interpretation guidance for each KPI.
• Establish dashboards that provide leadership and operational teams with actionable visibility.
• Track change failure rate, delivery quality, cycle time, adoption, and user experience.
• Use product and operational data to recommend improvements to workflows, features, and delivery practices.
• Ensure reported metrics are traceable, clearly defined, and aligned with program objectives.
• Define risk-assessment workflows spanning business policy, cybersecurity, privacy, data governance, architecture, and responsible AI.
• Identify security, compliance, operational, model, data, and implementation risks.
• Establish review checkpoints and approval requirements for AI-enabled capabilities.
• Document risks, mitigations, assumptions, limitations, exceptions, and accountable owners.
• Incorporate human review and escalation into high-impact AI-supported decisions.
• Collaborate with security, privacy, legal, architecture, and operational stakeholders as appropriate.
• Promote transparency in how AI recommendations and outputs are generated, reviewed, and used.
• Support ongoing governance as the platform expands to additional use cases.
• Produce solution architecture documents, workflow diagrams, product roadmaps, process maps, user guides, and implementation artifacts.
• Document integrations, data flows, business rules, security considerations, technical decisions, and operational dependencies.
• Maintain product requirements, user stories, acceptance criteria, backlog records, and release documentation.
• Create training, support, administration, and transition materials.
• Lead stakeholder demonstrations and product-readiness reviews.
• Deliver knowledge-transfer sessions for product, technical, administrative, and support teams.
• Facilitate pilot retrospectives and document lessons learned.
• Develop transition and expansion plans for future phases and related workstreams.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Business Analytics, or a related discipline.
• Equivalent professional experience may be considered in place of the degree requirement.
• Minimum eight years of experience in one or more of the following areas:
• Product management
• Solution architecture
• Digital transformation
• Enterprise application delivery
• Experience leading Agile, Scrum, Lean, or product-oriented delivery teams in accelerated delivery environments.
• Demonstrated experience delivering MVPs, proofs of concept, prototypes, or production solutions within compressed timelines.
• Experience managing product backlogs, sprint planning, roadmap development, release planning, and stakeholder communications.
• Experience designing or implementing enterprise workflow, intake management, request management, case management, service delivery, or process automation solutions.
• Experience with AI-native software engineering practices, including:
• AI-assisted development
• Prompt engineering
• Code generation
• Automated testing
• AI agents
• AI-augmented software delivery
• Experience using generative AI, large language models, retrieval-augmented generation, conversational AI, or comparable AI technologies.
• Experience defining enterprise solution architectures, API integrations, data flows, cloud services, and SaaS integrations.
• Experience with enterprise workflow, product-management, source-control, documentation, or DevOps platforms.
• Knowledge of Human-Centered Design, Design Thinking, and user-experience methodologies.
• Experience defining KPIs, adoption metrics, success measures, service-quality indicators, and performance dashboards.
• Understanding of cloud architecture, application integration, enterprise security, and software delivery practices.
• Strong requirements analysis, facilitation, communication, presentation, and stakeholder-management skills.
• Ability to communicate technical and AI concepts clearly to technical and nontechnical audiences.

Preferred Qualifications

• Experience supporting government, public-sector, regulated, or similarly complex enterprise organizations.
• Experience working across both AWS and Microsoft Azure environments.
• Experience leading digital transformation, workflow modernization, or AI-assisted decision-support initiatives.
• Experience designing recommendation engines, intelligent routing, risk-assessment workflows, or AI-powered intake solutions.
• Experience establishing measurement frameworks for AI-assisted or AI-augmented delivery models.
• Familiarity with responsible AI, AI governance, model risk, privacy, cybersecurity, and data-governance practices.
• Experience designing monitoring and consolidated management dashboards.
• Experience implementing ServiceNow request, workflow, case management, or service-delivery solutions.
• Experience integrating AI services with enterprise SaaS platforms and operational workflows.
• Experience facilitating executive workshops, user research, architecture reviews, and product demonstrations.
• Experience planning the transition of a pilot or prototype into long-term production operations.
• Experience developing adoption strategies, training materials, and knowledge-transfer plans.

Required Certification

Candidates should hold at least one relevant professional certification in a related discipline, such as:

• Artificial intelligence or machine learning
• Cloud computing
• Product management
• Agile or Scrum delivery
• DevOps
• Enterprise architecture
• ServiceNow
• AWS
• Microsoft Azure
• Google Cloud
• SAFe

Preferred Certifications

• AWS Certified Solutions Architect
• Microsoft Certified: Azure Solutions Architect Expert
• Microsoft Certified: Azure AI Engineer Associate
• Google Cloud Professional Cloud Architect
• Certified Scrum Product Owner
• Certified ScrumMaster
• SAFe Product Owner/Product Manager
• SAFe Architect
• ServiceNow Certified System Administrator
• ServiceNow Certified Implementation Specialist
• Certified Business Architect or enterprise architecture certification
• Product-management certification
• Responsible AI or AI-governance certification