AI Architect

• 7+ years of experience in AI Engineering or a related role
• Strong hands-on experience in Python development, including FastAPI, asyncio, pytest, type hints, and working within a monorepo structure
• Experience designing and implementing AI agent orchestration systems, including LLM gateways, tool gateways, streaming architectures (SSE and WebSocket), and agent-to-platform contracts
• Understanding of responsible AI governance, including capability boundaries, approval workflows, disclosure requirements, audit traceability, and regulatory control mapping
• Experience working with AWS cloud platforms in production environments
• Experience in rigorous testing practices, including unit, integration, and UAT/BDD testing, with enforced code coverage thresholds (≥90%)
• Proficiency with modern AI tooling, such as Cursor, MCP servers, prompt engineering techniques, and CLI automation tools (e.g., GitLab, Jira)
• Experience implementing observability using OpenTelemetry, Grafana or Phoenix, and structured logging practices
• Excellent command of written and spoken English (B2+ level)

Nice to have:

• Understanding of data architecture principles, including ontology design, taxonomy, conceptual/logical/physical modeling, data contracts, and gap analysis
• Experience designing and working with knowledge graphs in Neo4j, including dual-graph architectures, Cypher queries, graph sizing, versioning, and GraphRAG patterns
• Ability to design and implement domain-specific languages, including grammar definition, type systems, semantic validation, and execution pipelines
• Familiarity with security practices, such as PBAC/RBAC, JWT authentication, security context propagation, secret management, and secure code practices (SAST)
• Experience integrating cloud data platforms, including Snowflake and AWS services, such as EKS, IAM, and ALB, using Helm and GitOps tools like ArgoCD
• Ability to create and maintain architectural documentation using ADR/RFC processes and manage their lifecycle effectively
• Knowledge of formal methods, such as Alloy or TLA+ for high-assurance system design
• Experience with voice and multimodal AI systems, including STT/TTS pipelines using platforms like OpenAI or AWS Bedrock
• Understanding of modern documentation practices (e.g., Diátaxis) and structured knowledge management
• Familiarity with compliance frameworks, regulatory traceability, and adversarial testing methodologies
• Exposure to additional ontology and modeling standards, such as RDF, SHACL, SKOS, or financial taxonomies
• Experience with CI/CD quality gates, including tools like Bandit and Radon, and automated merge request review processes
• Experience building and productizing AI advisory solutions in regulated financial environments
• Understanding of end-to-end governance flows, including disclosure handling, capability restriction, human-in-the-loop approvals, and full audit chain implementation aligned with compliance requirements

• Own and drive the end-to-end architecture, from raw data ingestion to AI-powered advisory outputs
• Define and enforce clear contracts across all layers, including data assets, ontology and graph structures, financial DSL, LLM gateway, tool gateway, agent layer, and user experience
• Design systems with strong governance, ensuring all components are backed by documented architectural decisions (ADR/RFC) and traceable justification
• Build and deliver hands-on solutions, iterating incrementally while maintaining architectural consistency and quality
• Ensure all layers include robust verification mechanisms, including formal validation (where applicable), runtime checks, and auditability
• Collaborate with engineering, compliance, and client stakeholders to ensure the architecture meets regulatory, technical, and business requirements
• Maintain a defensible architecture aligned with responsible AI principles, including transparency, traceability, and controlled capability exposure