Solution Architecture Manager

Position Overview

As the Solution Architecture Manager, you will be a strategic, dual-focus technical leader driving the 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 (AI). 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, including Salesforce, Azure, and GCP, as well as their native AI/ML technologies, such as Azure AI Studio.
• AI/ML & Generative AI Ecosystems: Comprehensive knowledge of the AI lifecycle, foundation models (LLMs and 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 and warehouses, and model deployment, monitoring, and retraining strategies.
• Security, Privacy & AI Ethics: Knowledge of data privacy regulations, including CCPA; AI compliance frameworks, including NIST AI RMF and the EU AI Act; data residency; and techniques for mitigating AI bias, toxicity, and hallucinations.
• Architecture Frameworks & Methodologies: Familiarity with standard architectural frameworks, such as TOGAF and Zachman, and Agile/Scrum project management methodologies.
• FinOps & Cloud Economics: Understanding of cloud cost management, particularly AI compute cost structures involving GPUs and TPUs, as well as API token-based pricing models.

Abilities

• Business-to-Technical Translation: Ability to understand business stakeholder needs, identify operational bottlenecks, and translate requirements into viable, cost-effective AI and architectural solutions.
• Executive Communication: Ability to clearly and persuasively communicate complex AI concepts, architectural trade-offs, and return on investment (ROI) to C-suite executives and board members.
• Risk Anticipation & Mitigation: Ability to anticipate technical, security, and ethical risks in AI deployments and proactively design safeguards, such as human-in-the-loop workflows and data masking.
• Adaptability & Continuous Learning: Ability to adapt to evolving AI technologies, stay current with emerging research, and apply new developments to enterprise challenges.
• Cross-Functional Influence: Ability to build consensus among product managers, legal teams, security officers, and engineering leaders to support the successful deployment of AI services.

Technical Skills & Competencies

• System Design & Integration: Skill in designing scalable, secure, and resilient architectures that integrate AI models with existing enterprise software, legacy systems, and user interfaces.
• Technical Evaluation & Prototyping: Proficiency in evaluating vendor AI solutions, open-source models such as Hugging Face models, and third-party APIs to determine the best build-versus-buy approach for business use cases.
• Technical Leadership & Mentoring: Ability to manage, coach, and evaluate cross-functional technical teams, including cloud architects, data scientists, machine learning engineers, and software developers.
• Strategic Roadmapping: Skill in developing and maintaining a multi-year technical vision and AI/architecture roadmap aligned with broader business objectives.
• Budgeting & Vendor Management: Skill in negotiating technical contracts, managing software vendor relationships, and optimizing departmental and project budgets.
• Software Development: Foundational proficiency in programming languages such as Python, SQL, Java, or Go to review architecture, guide engineering teams, and troubleshoot complex integrations.

Required Qualifications & Skills

• At least 3 years of experience in a lead role managing employees.
• Experience with artificial intelligence (AI).
• Experience with Jira or Azure DevOps.
• Experience with Agile methodologies.
• Bachelor's degree in Computer Science or Computer Engineering.

Preferred Skills

• TOGAF certification or experience with the TOGAF framework.
• Product management experience.

#LI-JS1