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.

Benefits

• Salary: $200k - $275k

• Comprehensive benefits package.