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.