Senior Machine Learning Engineer
Wand AI
full-time
Posted on:
Location Type: Remote
Location: United States
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Job Level
About the role
- 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.
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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningML engineeringMLOpsPythondistributed systemsdata pipelinescloud platformsmodel servingagentic workflowscontinuous learning
Soft Skills
collaborationtroubleshootingindependencecommunicationknowledge sharingproblem-solvingorganizational skillsteamworkadaptabilityleadership