Solutions Architecture Lead

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, Google Cloud Platform), 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, g