Principal AI Architect
Financial services / wealth management client · Remote (US, CST Time)
- Location
- Remote (US, CST Time) · Remote
- Salary band
- Band not stated
- Type
- Full-time
- Level
- Principal
- Work authorization
- Not stated
Stack
Generative AI · RAG · Python · LLM · Anthropic Claude · OpenAI · Google Gemini · Vector Databases · Pinecone · pgvector · GCP · Azure · AWS · Kubernetes · Vertex AI · Snowflake · BigQuery · Databricks · Microservices · LangChain
About the role
Principal AI Architect Fulltime Position - US Remote (CST Time) Generative AI, RAG and Enterprise Data/Application Architecture | Hands-on | 15+ years Role Summary We are looking for a senior, hands-on AI Architect to design, build and govern production-grade Generative AI solutions for enterprise clients. You will combine deep data and application architecture expertise with practical LLM engineering: RAG, vector search, agentic patterns and multi-model strategy across Anthropic, OpenAI and Gemini. You will own solutions end to end, from target-state architecture and ARB approval through secure, governed delivery on cloud. Key Responsibilities • Define target-state AI, data and application architectures, reference patterns and reusable accelerators for enterprise GenAI programs. • Design and build RAG and agentic systems: ingestion, chunking, embeddings, hybrid retrieval, re-ranking, grounding, evaluation and observability. • Select and integrate LLMs (Anthropic Claude, OpenAI, Google Gemini) with sound model-routing, cost, latency, safety and fallback strategies. • Stay hands-on: write and review code, build proofs of concept, set engineering standards and unblock delivery teams. • Take architectures through Architecture Review Boards: prepare artifacts (ADRs, NFRs, logical and data architecture, integration flows), defend trade-offs and secure approvals on time. • Design security and access: RBAC/ABAC, identity integration, API management (APIM), secrets, network isolation, data classification, PII/PHI handling and audit trails. • Establish AI governance: prompt and model versioning, evaluation harnesses, guardrails, human-in-the-loop controls, lineage and responsible-AI practices. • Partner with data engineering on pipelines, semantic layers, metadata and data quality that make enterprise data AI-ready. • Advise client executives and engineering leaders, mentor architects and engineers, and contribute to pre-sales and solution shaping. Required Qualifications • Experience: 15+ years in software, data and solution architecture, including at least 3 years designing and delivering production GenAI/LLM solutions. • Hands-on engineering: Strong Python (plus Java, TypeScript or similar), REST/GraphQL APIs and event-driven integration. Current in code, not only diagrams. • RAG and vector search: Demonstrated delivery of RAG systems with vector databases (e.g., Pinecone, pgvector, Azure AI Search, Vertex AI Vector Search, OpenSearch, Weaviate or Milvus). Retrieval quality tuning and evaluation. • LLM platforms: Hands-on with Anthropic, OpenAI and Gemini APIs, including tool/function calling, structured outputs, prompt engineering, context management and cost control. • Cloud: Production experience on GCP (strongly preferred), Azure or AWS: compute and containers (Kubernetes), managed AI services (Vertex AI, Azure OpenAI/AI Foundry, Bedrock), storage, serverless and CI/CD. • Data architecture: Data modelling, lakehouse/warehouse patterns, ingestion pipelines, metadata, lineage and data quality. Snowflake, BigQuery or Databricks experience. • Application architecture: Microservices, API design, integration patterns, resilience and non-functional requirements. • Security and governance: RBAC, identity (OAuth2/OIDC, Entra ID, IAM), APIM gateways, encryption and compliance (e.g., HIPAA, GDPR, SOC 2). • Architecture governance: Proven track record presenting to and securing approval from ARBs, with clear ADRs and trade-off analysis. • Communication: Clear written and verbal communication with executives and engineers. Experience leading without direct authority. Preferred Qualifications • Agentic frameworks and orchestration (LangGraph, LangChain, LlamaIndex, MCP, Claude Agent SDK, Google ADK). • Knowledge graphs, ontology or GraphRAG (e.g., Neo4j). • LLMOps: evaluation frameworks, observability (e.g., LangSmith, Arize, Langfuse), guardrails and red-teaming. • Experience with AI-assisted engineering tools such as Claude Code for code generation and migration. • Cloud or architecture certifications (GCP Professional Cloud Architect, Azure Solutions Architect, AWS Solutions Architect, TOGAF). • Consulting or professional-services background, and experience building reusable accelerators. • Bachelor's or Master's degree in Computer Science, Engineering, Data Science or equivalent experience.
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