Client: *** Fortune 500 Financial Services, Chicago ***

Location: 100% remote position.

Pay: $195,000-210,000

About the Role

We're looking for an AI Engineer to join our technology team and help us build the next generation of intelligent systems for our business. This is a hands-on role where you'll design, develop, and deploy AI agents and generative applications that solve real problems across trading, risk, compliance, and client services.

You won't just be experimenting with the latest models – you'll be shipping production systems that need to work reliably in a regulated environment. If you're excited about building autonomous agents that can reason, plan, and take action, and you want to do it somewhere your work actually matters, we'd like to hear from you.

What You'll Be Doing

Designing and building agentic AI systems that can handle complex, multi-step workflows with minimal human intervention
Developing generative AI applications for document analysis, report generation, client communications, and internal knowledge retrieval
Integrating large language models with our existing infrastructure, data systems, and APIs
Building robust evaluation frameworks to measure agent performance, accuracy, and safety
Working closely with quants, traders, operations teams, and compliance to understand their needs and translate them into working solutions
Implementing appropriate guardrails, monitoring, and human-in-the-loop controls for high-stakes applications
Staying current with a rapidly evolving field and bringing new techniques and tools to the team
Contributing to our internal AI platform and tooling to accelerate development across the firm

What We're Looking For

Must have:

3+ years of software engineering experience, with at least 1-2 years focused on AI/ML systems
Strong Python skills and experience with modern ML frameworks
Hands-on experience building applications with LLMs (OpenAI, Anthropic, open-source models)
Familiarity with agent frameworks and orchestration patterns (LangChain, LlamaIndex, AutoGen, or similar)
Experience with RAG architectures, vector databases, and embedding models
Understanding of prompt engineering, fine-tuning approaches, and model evaluation
Comfortable working with APIs, microservices, and cloud infrastructure (AWS or Azure preferred)
Good communication skills – you can explain technical concepts to non-technical stakeholders

Nice to have:

Background in financial services or familiarity with trading, risk, or compliance workflows
Experience deploying ML systems in regulated industries
Knowledge of MLOps practices and tools (model versioning, monitoring, CI/CD for ML)
Exposure to function calling, tool use, and multi-agent coordination patterns
Experience with structured output generation and constrained decoding

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