AI 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.

• 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.

• 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.

• 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.