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Office in Poland: Gdańsk, Poland: Krakow
AI Engineering& 8 others
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We are looking for an experienced AI Architect to join our progressive team.
You’ll work with a multi-service platform designed for financial analysis, portfolio construction, and AI-assisted investment advisory. The platform combines a Snowflake-backed data lake with a Neo4j dual-graph architecture (domain ontology and lexical/GraphRAG), enabling unified querying of portfolios, financial instruments, and unstructured research through a single financial domain-specific language (DSL). The system supports both streaming chat and voice interfaces.
Responsibilities
- 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
Requirements
- 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