About the Role FactualIQ (FAIQ) builds next-generation Decision Engine workflows that integrate machine learning, agentic automation, and advanced reasoning tools into enterprise products that empower organizations to make better upside decisions faster.

As a Machine Learning Engineer (Product AI), you'll build ML/AI tools that power FAIQ's Decision Engine for our enterprise clients. You'll own the complete development lifecycle, from synthetic data generation and model development to deploying production APIs and services that autonomous agents will consume. Your work will include simulation pipelines, forecasting tools, RAG systems, and inference services that enable decision intelligence at scale.

What You’ll Do Design and deploy ML/AI tools and services that power FAIQ'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. Collaborate with platform and agent teams to understand requirements, define tool interfaces, and ensure ML services integrate seamlessly into engine workflows.

Required Qualifications Bachelor's or Master's degree in Computer Science, Data Science, or related technical field (or equivalent practical experience). 5+ years of experience building and deploying ML models in production environments, with recent hands-on experience in LLM or GenAI systems. Expert-level Python programming with deep knowledge of ML frameworks (PyTorch, TensorFlow, or similar). Production experience with cloud ML platforms (AWS, GCP, or Azure) and MLOps tools (MLflow, Kubeflow, or similar). Experience with modern LLM and retrieval-based systems including RAG architectures, vector databases, and embeddings. Experience developing synthetic data generation pipelines or data simulation systems for ML training and evaluation. Demonstrated ability to build and deploy scalable model APIs and production ML infrastructure. Proficiency with SQL, data pipelines, and feature engineering workflows. Familiarity with CI/CD practices, containerization (Docker), and version control (Git).

Preferred Qualifications Expertise in forecasting, time series, network analytics, optimization, or reinforcement learning. Experience with LLM orchestration frameworks (LangChain, LlamaIndex, DSPy), prompt engineering strategies, or multi-modal LLM applications. Experience with advanced vector database optimization, hybrid search strategies, or embedding model customization. Experience with distributed model training, model optimization at scale, or high-performance production ML systems. Knowledge of bias detection, hallucination mitigation, prompt injection defenses, or compliance frameworks. Cloud ML certification, open-source contributions, or published ML research.

We value diverse perspectives and encourage all qualified candidates to apply, even if you don't match every qualification perfectly.

We are currently seeking candidates who are legally authorized to work in the United States or Canada. Preference will be given to applicants located in Washington, Oregon, or British Columbia. We are committed to providing equal employment opportunities and do not discriminate based on race, color, religion, sex, national origin, age, disability, or genetic information.

Salary Range: Up to $137,500 USD (US) / $190,000 CAD (Canada), depending on experience and location.