Solutions Architect

Responsibilities
Use-case discovery and prioritization: Facilitate business and technical discovery, assess whether AI is appropriate, define expected outcomes, and prioritize opportunities by value, feasibility, risk, and adoption readiness.
Solution blueprinting: Translate business needs into platform-neutral solution options covering generative AI, agentic workflows, RAG, automation, predictive models, or hybrid patterns.
Detailed architecture: Create end-to-end designs for model interaction, orchestration, data and tool access, APIs, identity, observability, evaluation, security, and operational support.
Proof of concept: Build or directly guide prototypes that validate technical feasibility, user value, quality, performance, and key risks before scaled implementation.
Developer enablement: Provide design walkthroughs, reference patterns, technical decisions, code-level guidance, and reviews throughout delivery rather than relying on document-only handoffs.
Platform and model assessment: Evaluate cloud AI services, foundational models, agent frameworks, integration patterns, and supporting data platforms against enterprise requirements.
Responsible AI and governance: Embed privacy, security, auditability, human oversight, evaluation, content safety, and risk controls into architecture and delivery gates.
Stakeholder communication: Present architecture decisions, trade-offs, recommendations, and progress to engineering leaders, business stakeholders, risk partners, and executives.
Reusable assets: Develop reference architectures, templates, guardrail patterns, evaluation scorecards, and playbooks that improve future delivery speed and consistency.
Required Qualifications
10+ years of technology delivery experience, including significant solution architecture or technical leadership responsibility.
Proven experience designing and delivering production-grade AI solutions such as LLM applications, RAG systems, agentic workflows, intelligent automation, or ML-enabled products.
Hands-on software engineering capability in Python and/or a modern full-stack technology, including APIs, cloud-native services, integration, testing, and deployment practices.
Ability to translate loosely defined business problems into measurable use cases, architecture decisions, implementation increments, risks, and acceptance criteria.
Experience with prompt and context design, model evaluation, grounding approaches, tool/API integration, observability, and secure deployment patterns.
Strong knowledge of enterprise data architecture, SQL, data quality, semantic concepts, and data-access controls.
Understanding of responsible AI, privacy, security, model risk, and governance practices for sensitive enterprise data.
Clear written and verbal communication, including the ability to influence technical and executive audiences.
Experience mentoring engineers and performing architecture and code reviews in iterative delivery environments.
Preferred Qualitiffications
Experience with Microsoft Azure AI services, Azure AI Foundry, Azure OpenAI, Azure data services, or comparable cloud AI platforms.
Experience with Anthropic Claude, OpenAI-compatible APIs, Model Context Protocol, vector search, knowledge graphs, agent frameworks, and enterprise RAG patterns.
Full-stack experience with modern web frameworks, API gateways, containers, CI/CD, infrastructure as code, and production observability.
Experience in financial services, lending, collections, servicing, compliance, risk, dealer operations, or another highly regulated industry.
Familiarity with PII/NPPI controls, model validation, audit evidence, and human-in-the-loop approval patterns.
Cloud or AI architecture certifications.

Work arrangement
No