Job summary
Seeking a Senior AI Agentic Engineer to lead the architectural design and technical execution of AI agent frameworks within the financial technology sector. This hybrid role involves both product vision ownership and detailed system engineering, emphasizing the development of agent orchestration, integration, evaluation, and observability in a cloud environment. The position includes leading a small internal team, collaborating across operations and commercial teams, and ensuring regulatory compliance for client-facing AI deployments.
Location and work setup
- Location
- Oslo
- Work setup
- Hybrid
Responsibilities
Own the architecture and technical implementation of an AI agentic platform, including supervisor and multi-agent orchestration, tool integration, memory management, and error handling. Lead development using agentic frameworks such as LangGraph or LangChain. Design integration with backend systems primarily via SQL/T-SQL. Define evaluation processes and metrics for AI outputs, maintain observability and runtime quality using tools like Langfuse and CloudWatch. Provide technical leadership to the engineering team and coordinate with external partners transitioning delivery to internal ownership. Collaborate with operational and commercial teams to translate business processes into deployed AI agents, ensuring production readiness, security, and regulatory compliance. Engage cross-functionally with product management and infrastructure teams and represent the platform in technical and strategic discussions.
Qualifications
Minimum 8 years in software engineering with at least 3 years in building production AI systems using large language models or agentic frameworks beyond prototyping. Deep experience in the banking and wealth management domain, understanding financial products, workflows, and regulatory environments. Hands-on expertise with agentic AI frameworks such as LangGraph, LangChain, AutoGen, or CrewAI, including experience implementing supervisor and multi-agent patterns. Strong AWS cloud engineering skills with production experience on AI/ML services such as Bedrock, Lambda, and CloudWatch. Proven ability to design and operate evaluation methodologies for non-deterministic AI including dataset creation, regression testing, human review workflows, and metrics tracking.