Backbase
Financial Services
SeniorAIEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior AI Engineer at Backbase. Skills: Agentic Orchestration, RAG, Kubernetes, LangChain. Build underlying infrastructure. Build agent orchestration layers”
Industry & Context.
Debugging; Performance tuning
What They're Looking For.
Must Have
Expert-level Python skills, Experience using LangFuse, LangSmith, or Promptfoo, Ability to design and manage scalable, distributed systems, Experience with LangChain ecosystem, Proven track record building autonomous agents, Knowledge of RAG architectures
Nice to Have
Java knowledge is a plus, Familiarity with CI/CD pipelines, Familiarity with cloud-native logging/monitoring, Understanding of banking/financial services requirements
What You'll Do.
Build underlying infrastructure
Build agent orchestration layers
Build evaluative frameworks
Design agentic workflows
Implement agentic workflows
Build assistant platforms
Develop RAG pipelines
Optimize RAG pipelines
Develop GraphRAG pipelines
Optimize GraphRAG pipelines
Architect scalable AI services
Architect distributed AI services
Integrate AI services
Implement monitoring frameworks
Implement tracing frameworks
Implement evaluation frameworks
Implement Model Context Protocol
Design Human-in-the-Loop patterns
Provide technical leadership
Contribute to AI strategy
Contribute to best practices
How You'll Work.
Team & Collaboration
Cross-functional teams; Junior engineers
Full Job Description
Backbase is on a mission to help banks re-architect around the customer and embrace the era of Engagement Banking. We are looking for a Senior AI Engineer to join our AI you will be building the underlying infrastructure, agent orchestration layers, and evaluative frameworks that make AI reliable, safe, and impactful in a highly regulated financial environment. Key Responsibilities Agentic Orchestration: Design and implement complex agentic workflows and assistant platforms using the LangChain ecosystem. Advanced Retrieval: Develop and optimize RAG (Retrieval-Augmented Generation) and GraphRAG pipelines to provide agents with deep, contextual domain knowledge. System Design: Architect scalable, distributed AI services that integrate seamlessly into our existing Kubernetes (K8s) environments. Agentic Ops (AIOps): Implement robust monitoring, tracing, and evaluation frameworks (LangSmith, LangFuse, Promptfoo) to ensure the reliability and performance of LLM applications. Skill Integration: Build and manage "Skills" and toolsets for agents, including the implementation of the Model Context Protocol (MCP) for seamless data/tool interaction. Human-in-the-Loop (HIL): Design sophisticated HIL patterns to ensure high-stakes financial decisions remain governed and accurate. Mentorship: Provide technical leadership to junior engineers and contribute to the internal AI strategy and best practices. Technical Requirements Expertise in LangChain: Deep, hands-on experience with the LangChain ecosystem (LangGraph, LangServe) for building production-grade LLM applications. Agentic Mastery: Proven track record of building autonomous agents or sophisticated assistant platforms using modern techniques (Tools, Skills, MCP). AI Infrastructure: Strong knowledge of RAG architectures and emerging GraphRAG methodologies. Python Proficiency: Expert-level Python skills with a focus on writing clean, maintainable, and asynchronous code. System Architecture: Demonstrated ability to design and ma
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