Senior Python-AI Engineer Agentic Systems

Responsibilities

• Design and build multi-agent systems where autonomous agents collaborate to solve complex, real-world business problems across financial platforms.
• Develop and optimize Retrieval-Augmented Generation (RAG) architectures, including embedding strategies, vector databases, and retrieval pipelines, to improve the accuracy and reliability of AI-generated outputs.
• Implement planning and reasoning capabilities using knowledge graphs, rule-based reasoning, and search algorithms that enable agents to execute multi-step workflows reliably.
• Construct resilient agent architectures with built-in mechanisms for error recovery, self-correction, and feedback-driven improvement to maintain performance in production environments.
• Integrate large language models (LLMs), predictive models, and reasoning frameworks into agent systems that support complex financial decision-making workflows.
• Build APIs, tools, and microservices that connect AI capabilities with enterprise applications, enabling seamless integration at scale.
• Optimize AI systems for low latency and high throughput, applying techniques such as response streaming, caching, and token usage optimization to ensure performance and cost-effectiveness.
• Apply current AI research to real business challenges and collaborate with engineers across the team to elevate AI development practices and foster engineering excellence.
• Stay current with state-of-the-art research and apply it to solve business problems, identifying opportunities for innovation.
• Collaborate with and mentor other engineers on AI best practices, fostering a culture of engineering excellence within the team.

Requirements

• 4 or more years of professional experience in software development and system design, with a demonstrated history of delivering large-scale production systems.
• Proficiency in Python and SQL, with hands-on experience building production-quality Agentic AI solutions using frameworks such as LangChain, LlamaIndex, or equivalent tools.
• Practical experience developing multi-agent systems using frameworks such as Google ADK, LangGraph, AutoGen, or CrewAI, including implementation of planning, reasoning, and memory systems.
• Advanced skills in Prompt and Context Engineering, with the ability to structure, compress, and optimize information for consistent and high-quality model performance.
• Experience working with Vector Databases as part of AI retrieval and memory architectures.

Qualifications

• Master's degree in Computer Science or a closely related field.
• Experience working within the financial services industry, particularly in areas such as Funds Transfer, Pricing, Hedging, or Treasury technology.
• Proficiency in Java as a secondary programming language alongside Python.