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Machine Learning Engineer

Build the ML/AI systems powering FactualIQ’s enterprise Decision Engine. Design scalable AI tools, deploy production-grade models, and enable intelligent agent workflows that drive mission-critical decision-making for enterprise clients.

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About the Role

As a Machine Learning Engineer, you’ll build ML/AI tools that power FactualIQ’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 consume. Your work will include simulation pipelines, forecasting tools, RAG systems, and inference services that enable decision intelligence at scale.

This is a hands-on technical role where you’ll work alongside senior engineers and cross-functional teams to build reliable, performant ML systems that support client-critical AI decision-making for Fortune 1000 clients, while growing your expertise in advanced ML engineering for enterprise AI applications.

What You’ll Do

  • 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.

What You’ll Bring

  • 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) and performance considerations for production workloads.
  • Production experience with cloud ML platforms (AWS, GCP, or Azure) and MLOps tools (MLflow, Kubeflow, or similar) including model deployment, monitoring, and lifecycle management.
  • Experience with modern LLM and retrieval-based systems including RAG architectures, vector databases, and embeddings, with understanding of chunking strategies, context optimization, and retrieval quality evaluation.
  • 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, including versioning, error handling, and observability.
  • Proficiency with SQL and data pipeline tools, including feature engineering workflows and understanding of data modeling trade-offs for ML use cases.
  • Familiarity with CI/CD practices, containerization (Docker), and version control (Git).
  • Experience working in multi-tenant or enterprise environments with awareness of data isolation, security requirements, and compliance considerations.
  • Strong communication skills and ability to collaborate effectively with cross-functional engineering teams and stakeholders.

What You Might Bring

  • 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 for enterprise AI.
  • Experience mentoring junior engineers or contributing to team knowledge-sharing and technical onboarding.
  • Contributions to open-source projects, technical blog posts, or community involvement in ML engineering topics.
  • Cloud ML certification (AWS ML Specialty, GCP Professional ML Engineer, or similar) or published ML research.

What WE VALUE

  • A growth mindset, building on the recognition that a good engineer is always learning.
  • Creative, entrepreneurial flexibility to try innovative approaches to solving problems, coupled with the resilience to recognize mistakes quickly, adapt and correct course as needed to achieve success.
  • Speed to solutions, with rapid, well-planned iterations.
  • Design-forward approaches to building technology products, coupled with a test-heavy technique to ensure that both the problem to be solved and the solution context are clear and optimal before development begins.
  • Transparent, frequent and constructive communication skills and practices.
  • Low-ego collaboration, where feedback is valued, everyone’s voice is heard, debates and disagreements are used for the team’s benefit, and commitment matters.
  • Mission alignment and care for delivering highest-standard quality to support our clients’ success.

Reporting

The role currently reports to FactualIQ’s Chief Strategy Officer, with a dotted line to a Senior Machine Learning Engineer. As we build out our engineering team, the role will ultimately report to a Tech Lead or Senior Tech Lead.

Career progression from this role will lead to a Senior Machine Learning Engineer position.

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