ML Engineer
Auburn Hills, MI
Apply now: ML Engineer (Staff Level Engineer), location is Hybrid (Auburn, Michigan). The start date is ASAP for this 6-month contract-to-hire position.
Job Title: ML Engineer
Location-Type: Hybrid ( Auburn, Michigan – 3 days onsite)
Start Date Is: ASAP
Duration: 6-Month Contract-to-Hire
Compensation Range: $70/hr – $90/hr (W2)
Job Description:
Lead the design and deployment of scalable, production-grade machine learning systems across IoT-enabled consumer products, owning the full AI lifecycle from data ingestion to production monitoring.
Day-to-Day Responsibilities:
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Architect and build end-to-end ML pipelines (data ingestion, feature engineering, training, deployment).
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Develop and deploy production AI/ML models in AWS environments.
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Partner with product, engineering, and executive stakeholders to translate business needs into ML solutions.
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Implement MLOps best practices (CI/CD, monitoring, drift detection, model versioning).
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Work with large, messy, unstructured IoT datasets to drive insights and automation.
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Mentor engineers and data scientists on AI engineering standards and best practices.
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Conduct code reviews and ensure production-quality AI systems.
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Collaborate with DevOps for model deployment and scalability.
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Must-Haves:
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6–8 years of experience in Machine Learning Engineering.
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Strong Python development experience.
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Deep experience with AWS (S3, EC2, Lambda, ECS, DynamoDB, CloudTrail).
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Experience with AWS SageMaker and/or Amazon Bedrock.
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Hands-on experience building production-grade ML systems (not just experimentation).
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Experience designing scalable ML architectures and MLOps frameworks.
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Background working with IoT, consumer electronics, or high-volume telemetry data.
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Strong communication skills; ability to work with C-level stakeholders.
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Nice-to-Haves:
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Experience in AI enablement for consumer-facing products.
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Systems engineering or end-to-end architectural background.
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Experience leveraging open-source ML models.
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Experience working in Agile/SaFe environments.
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Interest in pet-tech or connected device ecosystems.
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