Job description

Position Information Position Type Staff Augmentation Hours/Week Billing Type Hourly Shift Day Projected Start Date 11/02/2026 Start Time 8:00 AM Projected End Date/Duration 06/30/2027 End Time 5:00 PM Bid Limit Per Vendor 2 Number of Positions 1 Desired Career Level None Selected Minimum Education Level Bachelor's Degree Weekend None Position Location Address 1400 W. Washinton St. City Phoenix State Arizona Zip 85007 Country UNITED STATES Requirements Travel Required No Security Required Yes Onboarding Category The following are required for bid submission: • Resume • R2R • Cover Letter • Meets Work Location Position Description Hybrid 4 days a week. Local ONLY. As the Solution Architecture Manager, you will be a strategic, dual-focus technical leader driving our next generation of enterprise systems. In this highly visible role, you will bridge the gap between traditional enterprise architecture and the rapidly evolving landscape of artificial intelligence. You will not just oversee systems; you will be responsible for building modern, scalable software solutions with AI at their core. Knowledge: Enterprise & Cloud Architecture: Deep understanding of distributed systems, microservices, API-first design, and major cloud platforms (Salesforce, Azure, GCP), including their native AI/ML stacks (e.g. Azure AI Studio). AI/ML & Generative AI Ecosystems: Comprehensive knowledge of the AI lifecycle, Foundation Models (LLMs, SLMs), Natural Language Processing (NLP), computer vision, vector databases, and retrieval-augmented generation (RAG) architectures. MLOps & Data Engineering: Understanding of CI/CD for machine learning (MLOps), data pipelines, data lakes/warehouses, and model deployment, monitoring, and retraining strategies. Security, Privacy & AI Ethics: Knowledge of data privacy regulations (CCPA), AI compliance frameworks (NIST AI RMF, EU AI Act), data residency, and techniques for mitigating AI bias, toxicity, and hallucinations. Architecture Frameworks & Methodologies: Familiarity with standard architectural frameworks (e.g., TOGAF, Zachman) and Agile/Scrum project management methodologies. FinOps & Cloud Economics: Understanding of cloud cost management, particularly the cost structures associated with AI compute (GPUs, TPUs) and API token-based pricing models. Abilities Innate or acquired capacities to perform tasks and handle scenarios. Business-to-Technical Translation: The ability to listen to non-technical business stakeholders, identify operational bottlenecks, and translate those needs into viable, cost-effective AI and architectural solutions. Executive Communication: The ability to articulate complex AI concepts, architectural trade-offs, and ROI to C-suite executives and board members clearly and persuasively. Risk Anticipation & Mitigation: The ability to foresee technical, security, or ethical risks in AI deployments and proactively design safeguards (e.g., human-in-the-loop workflows, data masking). Adaptability & Continuous Learning: The ability to pivot rapidly in response to the fast-paced evolution of AI technologies, continuously absorbing new research and applying it to enterprise challenges. Cross-Functional Influence: The ability to lead by influence rather than just authority, driving consensus among product managers, legal teams, security officers, and engineering leads to ensure smooth deployment of AI services. Bachelors in Computer Science or Computer Engineering Proficiencies and techniques developed through training and experience. System Design & Integration: Skill in designing scalable, secure, and resilient architectures that seamlessly integrate AI models into existing enterprise software, legacy systems, and user interfaces. Technical Evaluation & Prototyping: Proficiency in assessing vendor AI solutions, open-source models (e.g., Hugging Face), and third-party APIs to determine the best build vs. buy approach for business use cases. Technical Leadership & Mentoring: Skill in managing, coaching, and evaluating cross-functional technical teams, including cloud architects, data scientists, machine learning engineers, and software developers. Strategic Roadmapping: Skill in creating and maintaining a technical vision and multi-year AI/architecture roadmap that aligns with the broader business objectives. Budgeting & Vendor Management: Skill in negotiating technical contracts, managing software vendor relationships, and optimizing departmental and project budgets. Software Development (High-Level): Foundational skill in relevant programming languages (e.g., Python, SQL, Java, or Go) to effectively review architecture, guide engineering teams, and troubleshoot complex integrations. Required Skills Skill Type Skill Name At least 3+ years as a Lead experience managing employees AI experience Jira or Devops experience Agile Methodology Preferred Skills Skill Type Skill Name TOGAF Product Management By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails from and its affiliates, and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel and HELP for help. You can access our privacy policy at Privacy Policy - Abacus .

Requirements

Must-haves

Position Information
Position Type
Staff Augmentation
Billing Type
Projected Start Date
Projected End Date/Duration
Bid Limit Per Vendor
Number of Positions
Desired Career Level
None Selected
Minimum Education Level
Bachelor's Degree
Position Location
1400 W. Washinton St.
Travel Required
Security Required
Onboarding Category
Cover Letter
Meets Work Location
Position Description
Hybrid 4 days a week. Local ONLY.
Enterprise & Cloud Architecture: Deep understanding of distributed systems, microservices, API-first design, and major cloud platforms (Salesforce, Azure, GCP), including their native AI/ML stacks (e.g. Azure AI Studio).
MLOps & Data Engineering: Understanding of CI/CD for machine learning (MLOps), data pipelines, data lakes/warehouses, and model deployment, monitoring, and retraining strategies.
Security, Privacy & AI Ethics: Knowledge of data privacy regulations (CCPA), AI compliance frameworks (NIST AI RMF, EU AI Act), data residency, and techniques for mitigating AI bias, toxicity, and hallucinations.

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Effort: medium

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