As an AI Solutions Engineer, you will be responsible for the design, development, and orchestration of GenAI-powered applications and autonomous agents. You will build the middleware and frameworks that allow our Data Science and Product teams to leverage Large Language Models (LLMs) effectively, ensuring these intelligent services are scalable, reliable, and integrated into our fintech ecosystem.
NATURE OF WORK
- Orchestrate Agentic Workflows: Design and implement multi-agent systems and complex chains (e.g., using LangGraph, CrewAI, or AutoGen) to automate sophisticated workflows.
- MCP Servers: Design and develop MCP servers to provide the agentic capabilities of agents to become autonomous and deliver concrete impact
- RAG Architecture: Develop and maintain robust Retrieval-Augmented Generation (RAG)pipelines, optimizing vector databases and semantic search to provide LLMs with accurate, real-time context
- Prompt Engineering & Management: Establish best practices for prompt versioning, evaluation (LLM-as-a-judge), and optimization to ensure consistent and safe model outputs
- API & Microservice Integration: Build and wrap GenAI capabilities into high-performance microservices (FastAPI/Python) that can be easily consumed by front-end and core banking systems
- Operationalize GenAI (LLMOps): Implement monitoring for hallucinations, token usage/cost, and latency. Build CI/CD pipelines specifically for AI agents, including automated evals (evaluation sets)
- Architecture & Governance: Provide guidance on the competitive advantage of different LLM architectures and ensure all AI solutions adhere to financial data privacy and security standards
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REQUIRED QUALIFICATIONS
- With at least a bachelor's degree in any quantitative discipline (i.e. Computer Science, Math, Physics, etc)
- Having relevant experience in creating building and maintaining AI agents or agentic workflows
- Having at least 1 year of experience in managing stakeholders
