AI Engineer – Agentic AI & Innovation
About The Position
A leading financial services organization is seeking a hands-on AI Engineer to join an Innovation & AI team focused on building and advancing next-generation Generative AI and Agentic AI solutions. This is a highly technical, hands-on role for an engineer who enjoys taking emerging business concepts and rapidly turning them into working prototypes. You will design intelligent agents, AI-enabled applications, and multi-step workflows that interact with enterprise data, APIs, metadata, applications, and analytical tools. The ideal candidate brings strong Python and TypeScript development experience, hands-on expertise with modern AI agent frameworks such as LangGraph and LangChain, and experience collaborating within modern Git/GitHub-based development environments.
Requirements
- Hands-on experience building Generative AI, Agentic AI, machine learning, or advanced software solutions
- Strong development experience with Python and TypeScript
- Experience building applications or intelligent agents powered by large language models (LLMs)
- Hands-on experience with LangGraph, LangChain, or comparable agent orchestration frameworks
- Experience with tool calling, agent orchestration, state management, context engineering, structured outputs, and multi-step AI workflows
- Experience with Azure OpenAI or another enterprise AI platform
- Strong experience developing and integrating REST APIs and services
- Experience integrating AI applications with enterprise data, applications, APIs, metadata, or analytical tools
- Knowledge of SQL and structured/unstructured data
- Working knowledge of graph-based data structures, knowledge graphs, or metadata-driven applications
- Strong Git/GitHub experience and familiarity working collaboratively within shared codebases
- Ability to independently take an ambiguous or emerging business concept from idea to functioning prototype
- Ability to build modular, documented, testable, and maintainable solutions
- Strong communication skills with the ability to explain technical decisions, risks, limitations, and tradeoffs
Nice To Haves
- DataHub, enterprise metadata platforms, knowledge graphs, graph databases, or semantic data layers
- Multi-agent systems, enterprise copilots, or AI-enabled decision-support tools
- React, JavaScript, MongoDB, or additional full-stack development experience
- Agent evaluation, observability, tracing, guardrails, human-in-the-loop workflows, or AI cost monitoring
- Cloud-native development, containerization, CI/CD, and automated deployments
- Experience working across multiple foundation models and evaluating model-selection tradeoffs
- GitHub Copilot or other AI-assisted software engineering tools
- Financial services, Treasury, liquidity, funding, forecasting, risk, or regulatory experience
- Responsible AI, data governance, cybersecurity, model risk, or enterprise technology controls
Responsibilities
- Design and build Generative AI and Agentic AI prototypes, proofs of concept, and technical demonstrations
- Develop intelligent agents, enterprise copilots, and AI-enabled decision-support applications
- Build agentic workflows incorporating tool calling, orchestration, state management, context engineering, structured outputs, and multi-step reasoning
- Develop Python services, APIs, tools, automation, and reusable AI components
- Build lightweight full-stack experiences to test new AI interaction models
- Integrate AI applications with enterprise data, metadata, APIs, applications, and analytical platforms
- Enable AI agents to reason across relationships between data assets, systems, business processes, models, controls, and business concepts
- Evaluate LLMs, agent frameworks, orchestration approaches, and context-engineering techniques
- Assess solutions based on reliability, reasoning quality, latency, cost, security, and business value
- Work collaboratively within shared codebases using Git/GitHub and modern software development practices
- Document reusable engineering patterns, technical constraints, lessons learned, and recommendations
- Partner with business, architecture, data, cybersecurity, risk, and technology teams to help move successful prototypes toward enterprise adoption
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
