AI Solution Architect
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Architecture
- Posted
Skills
Must have
- 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
- 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 deploym
- Strong communication, facilitation and stakeholder-management skills; degree in computer science, engineering, data science or a related field, or equivalent experience.
Nice to have
- 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.
Full description
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.