Machine Learning Engineer

FactualIQ is a company that builds ML/AI systems for enterprise decision-making. As a Machine Learning Engineer, you will design and deploy ML tools and services, owning the full development lifecycle to support client-critical AI decision-making.

Responsibilities:

• Design and deploy ML/AI tools and services that power FactualIQ’s Decision Engine, a multi-agent workflow platform that integrates ML and LLM capabilities for enterprise decision-making
• Own the full ML lifecycle: feature engineering, model development, experimentation, A/B testing, deployment, and performance optimization at scale
• Build production-grade LLM and RAG-based tools for retrieval, reasoning, and inference that AI agents can call as part of automated workflows
• Create robust APIs and SDKs that expose ML models as reusable, production-grade services with clear contracts, error handling, and observability
• Develop synthetic data generation pipelines to create training datasets, accelerate model iteration, and enable rapid customization for client-specific use cases
• Implement model versioning, experiment tracking, and rollback procedures to ensure reproducibility and safe iteration across production deployments
• Build monitoring and observability systems for deployed models, including performance degradation detection, drift monitoring, and automated alerting
• Develop and maintain documentation for ML services, APIs, model architectures, and operational runbooks to support cross-team collaboration
• Collaborate with platform and agent teams to understand requirements, define tool interfaces, and ensure ML services integrate seamlessly into engine workflows
• Participate in sprint planning, technical design reviews, and team knowledge-sharing to contribute to FactualIQ’s engineering culture and delivery cadence
• Stay current with emerging best practices in ML engineering and AI systems, incorporating learnings into your work