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Wand AI logo
Wand AI

Meet Your Future Workforce.

Senior Machine Learning Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteSeniorTeam 51-200Since 2022H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

75 days ago

Salary

0

Seniority

Senior

Job Description

Senior Machine Learning Engineer

Wand AI

• Develop and maintain ML platforms and pipelines supporting autonomous, goal-driven AI agents. • Build systems for the full ML lifecycle, including agentic decision-making, task orchestration, and goal execution. • Integrate ML models with product logic and business workflows to operationalize AI capabilities. • Implement pipelines for experimentation, productionization, and continuous agentic learning. • Collaborate with data science and product teams to turn research outputs into production AI agents. • Design and optimize infrastructure for large-scale training, inference, and multi-agent coordination. • Implement observability and monitoring for ML pipelines, agent behaviors, and goal-driven execution. • Build systems for automated evaluation, drift detection, and retraining of AI models. • Ensure reliability, scalability, and operational excellence of ML services powering autonomous workflows. • Troubleshoot complex issues in ML pipelines, agentic systems, and distributed infrastructure. • Contribute to CI/CD and development workflows supporting ML lifecycle, agent orchestration, and model deployment. • Collaborate and share knowledge to improve implementation of agentic AI systems across teams.

Job Requirements

  • Hands-on experience building production ML systems integrated with product goals and business logic.
  • Expertise in ML engineering, agentic workflows, and MLOps practices.
  • Strong programming skills in Python and experience integrating ML with backend systems and autonomous workflows.
  • Experience deploying machine learning models at scale, including goal-driven or multi-agent systems.
  • Experience building ML infrastructure supporting training, experimentation, inference, and agent coordination.
  • Solid understanding of distributed systems, scalable data pipelines, and real-time agentic decision loops.
  • Experience designing ML systems on cloud platforms such as AWS, Azure, or GCP.
  • Experience with highly available model serving systems supporting autonomous agentic tasks.
  • Strong debugging and troubleshooting skills in complex ML and agentic AI production environments.
  • Ability to work independently and collaboratively within cross-functional teams.

Benefits

  • Health insurance
  • Professional development opportunities

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