AI Solution Architect

Job summary

Lead the architecture and delivery assurance of secure, responsible AI solutions across their full lifecycle, from use-case discovery and validation through production monitoring and retirement. Coordinate with business and technical stakeholders to assess options, establish controls and evaluation criteria, guide implementation, and ensure solutions deliver measurable value.

Job details

This is a remote position. About the Role Lead the end-to-end architecture and delivery assurance of secure, responsible, scalable AI solutions—from use-case discovery and experimentation to production monitoring and retirement. Partner with business, product, data science, engineering, operations, security, privacy, legal, risk and vendor teams to deliver measurable value through hands-on technical validation and sound architecture decisions. Key Responsibilities Translate business needs into AI use cases, feasibility assessments, measurable outcomes and acceptance criteria. Select predictive ML, generative AI, retrieval-augmented generation (RAG), agents, intelligent document processing or non-AI alternatives. Design models, prompts, data and knowledge pipelines, embeddings, vector/hybrid search, orchestration, applications, APIs, cloud and identity. Define access-aware retrieval, guardrails, fallback behaviour and human oversight. Compare build, buy and hosted, open-weight, customized or embedded AI options for quality, safety, data residency, licensing, latency, cost, control, vendor viability and lock-in. Validate designs through prototypes, prompt/retrieval tests, model evaluations and implementation reviews. Set release thresholds for accuracy, relevance, groundedness, hallucination, toxicity, robustness, latency and cost. Assess data fitness, provenance, consent, lineage and authorized use; address model risk, security threats, privacy, fairness, explainability, intellectual property and regulatory obligations. Guide MLOps/LLMOps, versioning, reproducibility, deployment, rollback and operational readiness. Monitor quality, drift, safety, usage and cost throughout the lifecycle. Maintain architecture decisions, intended use, limitations, risks, dependencies and review evidence. Align with enterprise standards and roadmaps, support required independent approvals, and assure implementation at lifecycle gates. Communicate trade-offs, facilitate workshops, mentor teams and develop reusable patterns and controls. Coordinate shared capabilities while respecting business, model, data, platform and control-owner accountabilities. Requirements Required Qualifications Typically 10+ years of relevant technology experience, with significant architecture responsibility and demonstrated delivery of production AI/ML solutions. End-to-end architecture experience spanning models, data, retrieval, applications, integration, cloud, security and operations. Strong knowledge of foundation models, RAG, embeddings, vector search, prompt orchestration, tool use and agents; practical understanding of classical ML, feature/data pipelines, evaluation and model failure modes. Experience with cloud AI services, model hosting, API/event integration, identity, networking, secrets, containers and scalable deployment; MLOps/LLMOps, observability and model monitoring. Working knowledge of responsible AI, AI security, privacy engineering, data governance, model risk and human oversight; ability to assess vendor documentation, prompts, evaluation evidence and deployment configurations. Strong communication, facilitation and stakeholder-management skills; degree in computer science, engineering, data science or a related field, or equivalent experience. Preferred Qualifications AI delivery experience in financial services, credit unions or other regulated industries. Azure AI, Azure Machine Learning, Azure OpenAI or comparable platforms; API management and infrastructure as code. Familiarity with model gateways, AI safety tools, vector databases, knowledge graphs, feature stores, model registries and AI observability; commercial copilots, SaaS and open-source AI evaluation. Enterprise architecture methods, recognized AI risk/security/governance frameworks, and relevant cloud, AI/ML, data, security or architecture certifications.

What you’ll do

Lead the architecture and delivery assurance of secure, responsible AI solutions across their full lifecycle, from use-case discovery and validation through production monitoring and retirement. Coordinate with business and technical stakeholders to assess options, establish controls and evaluation criteria, guide implementation, and ensure solutions deliver measurable value.

Requirements

Requires typically 10+ years of relevant technology experience, significant architecture responsibility, and demonstrated delivery of production AI/ML solutions, with end-to-end expertise across models, data, retrieval, applications, cloud, security, and operations. Candidates should have practical knowledge of generative AI and classical ML, MLOps/LLMOps, responsible AI, privacy, and governance, along with strong communication skills and a relevant degree or equivalent experience.

Listed skills

• Machine learning · Preferred
• prompt engineering · Preferred
• Stakeholder Management · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• AI Solution Architecture
• Machine Learning
• Generative AI
• Retrieval-Augmented Generation
• Prompt Engineering
• Vector Search
• AI Evaluation
• Cloud Architecture
• MLOps
• LLMOps
• Responsible AI
• AI Security
• Data Governance
• Privacy Engineering
• Model Monitoring
• Stakeholder Management

Job areas

• Technology
• Software
• Data & Analytics
• Security & Safety
• Finance & Accounting

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