ML Engineer

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:

• Architect and build end-to-end ML pipelines (data ingestion, feature engineering, training, deployment).

• Develop and deploy production AI/ML models in AWS environments.

• Partner with product, engineering, and executive stakeholders to translate business needs into ML solutions.

• Implement MLOps best practices (CI/CD, monitoring, drift detection, model versioning).

• Work with large, messy, unstructured IoT datasets to drive insights and automation.

• Mentor engineers and data scientists on AI engineering standards and best practices.

• Conduct code reviews and ensure production-quality AI systems.

• Collaborate with DevOps for model deployment and scalability.

• Must-Haves:

  • 6–8 years of experience in Machine Learning Engineering.

  • Strong Python development experience.

  • Deep experience with AWS (S3, EC2, Lambda, ECS, DynamoDB, CloudTrail).

  • Experience with AWS SageMaker and/or Amazon Bedrock.

  • Hands-on experience building production-grade ML systems (not just experimentation).

  • Experience designing scalable ML architectures and MLOps frameworks.

  • Background working with IoT, consumer electronics, or high-volume telemetry data.

  • Strong communication skills; ability to work with C-level stakeholders.

• Nice-to-Haves:

  • Experience in AI enablement for consumer-facing products.

  • Systems engineering or end-to-end architectural background.

  • Experience leveraging open-source ML models.

  • Experience working in Agile/SaFe environments.

  • Interest in pet-tech or connected device ecosystems.