Join a growing FinTech team in Colorado (onsite) building production-grade multi-agent AI systems. This Staff Machine Learning Engineer role focuses on delivering reliable, enterprise-ready AI across advisor copilot workflows, investment intelligence, workflow automation, and autonomous research, with a strong emphasis on evaluation, observability, and cost-aware performance.
What you’ll build
- Production multi-agent systems using modern agent frameworks and large language models (LLMs).
- LLM/agent workflows that combine context retrieval, reasoning, tool execution, validation, and compliance controls.
- Distributed orchestration services for agent execution, focused on observability, monitoring, resilience, and fault tolerance.
- Evaluation frameworks to measure reasoning quality, accuracy, groundedness, hallucination mitigation, and financial correctness.
- Memory and context handling strategies, including scalable approaches for agent memory, context management, and persistent agent state.
- AI infrastructure evolution across model serving, orchestration, vector databases, caching, and cloud-native architecture.
How you’ll work
You’ll collaborate with Product, Design, and Engineering partners to translate business requirements into scalable AI solutions, then continuously optimize for performance, latency, reliability, scalability, and cost efficiency.
What you bring
- 6+ years of experience in Machine Learning, including 2–3 years building and deploying Generative AI or LLM-powered applications in production.
- Proven experience designing, developing, and implementing production-ready multi-agent AI systems.
- Strong expertise in multimodal LLMs, agent frameworks, knowledge graphs, reinforcement learning, model fine-tuning, agent memory, and synthetic data generation.
- Advanced Python skills with hands-on experience using modern AI and machine learning frameworks.
- Experience building distributed systems and deploying cloud-native applications across AWS, Azure, or Google Cloud Platform (GCP).
- Strong understanding of AI monitoring, evaluation frameworks, reliability engineering, and model performance optimization.
- Ability to design scalable, enterprise-grade AI architectures integrating multiple models, services, and workflows.
- Fast-paced startup or scale-up experience is highly desirable.
Tools you’ll use
Python, LLMs, Generative AI, multimodal LLMs, agent frameworks, knowledge graphs, reinforcement learning, model fine-tuning, vector databases, and cloud platforms across AWS, Azure, and Google Cloud Platform (GCP).
Compensation & benefits
- Salary: USD 200,000 – 275,000 per year
- Comprehensive benefits package
