Solution Architect

Anblicks is seeking an AI Solution Architect to lead the evaluation, architecture, prototyping, and delivery enablement of AI-powered business solutions. This is a hands-on role for an architect who can move from an ambiguous business problem to a practical solution blueprint, validate feasibility through a proof of concept, and guide developers through implementation. The role spans generative AI, agentic systems, intelligent automation, predictive solutions, data integration, and responsible AI controls.

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.