Staff Machine Learning Engineer

Colorado

Machine Learning

Permanent

Our client, a growing FinTech company, are hiring a Staff Machine Learning Engineer to join their team in Colorado. The successful candidate will play a key role in designing and building production-grade, multi-agent AI systems that power advisor copilots, investment intelligence, workflow automation and autonomous research.

Responsibilities

Design, develop and deploy production-grade multi-agent AI systems using modern agent frameworks and large language models (LLMs).
Build intelligent AI workflows that combine context retrieval, reasoning, tool execution, validation and compliance controls.
Develop scalable distributed services for agent orchestration, with a focus on observability, monitoring, resilience and fault tolerance.
Create evaluation frameworks to assess reasoning quality, accuracy, groundedness, hallucination mitigation and financial correctness.
Implement scalable approaches to memory management, context handling and persistent agent state.
Collaborate closely with Product, Design and Engineering teams to translate business requirements into scalable AI solutions.
Continuously optimize AI systems for performance, latency, reliability, scalability and cost efficiency.
Influence the design and evolution of AI infrastructure, including model serving, orchestration, vector databases, caching and cloud-native architecture.

Skillset

At least 6 years of experience in Machine Learning, including 2-3 years building and deploying Generative AI or LLM-powered applications in production environments.
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 programming 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 system monitoring, evaluation frameworks, reliability engineering and model performance optimization.
Demonstrated ability to design scalable, enterprise-grade AI architectures that integrate multiple models, services, and workflows.
Previous experience working in a fast-paced startup or scale-up environment is highly desirable.