Senior Machine Learning Engineer (AI Systems)

Insight Global • United State
Relocation
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AI Summary

Seeking a Senior Machine Learning Engineer to own the design, deployment, and evolution of AI systems. This role focuses on system-level ownership, technical decision-making, and mentorship, requiring end-to-end ML system experience in production. Key responsibilities include designing AI systems with Generative AI, RAG, and Agentic AI, and partnering with stakeholders.

Key Highlights
System-level ownership of AI systems
Design and deployment of Generative AI, RAG, and Agentic AI
Mentorship of ML Engineers and partnership with stakeholders
Key Responsibilities
Design and implement AI systems using Generative AI, RAG, and Agentic AI
Architect end-to-end ML solutions with awareness of technical and business tradeoffs
Own system-level performance, reliability, and safety in production
Deploy improvements and manage releases for ML systems
Mentor ML Engineers on best practices and technical decision-making
Partner with business stakeholders and vendors to evaluate and integrate AI tools
Technical Skills Required
Generative AI RAG Agentic AI Time-series models NLP LLMs Python TensorFlow Pytorch Scikit-learn MLOps Model monitoring CI/CD
Benefits & Perks
healthcare insurance offerings
paid leave
Bonus
Nice to Have
scalable architectures
Data Quality and Governance

Job Description


SR ML Engineer

Livonia, MI - Relocation assistance available

Fulltime: $130k - $150k + Bonus + Benefits

** Benefit packages for this role may include healthcare insurance offerings and paid leave as provided by applicable law.


Insight Global is seeking a Senior Machine Learning Engineer to own the design, deployment, and evolution of AI systems supporting core business functions. This role goes beyond model development and focuses on system‑level ownership, technical decision‑making, and mentorship. The ideal candidate has experience building ML systems end‑to‑end in production and is comfortable making architectural tradeoffs that balance performance, risk, and scalability.

  • Design and implement AI systems using Generative AI, RAG, and Agentic AI
  • Architect end‑to‑end ML solutions with awareness of technical and business tradeoffs
  • Own system‑level performance, reliability, and safety in production
  • Deploy improvements and manage releases for ML systems
  • Mentor ML Engineers on best practices and technical decision‑making
  • Partner with business stakeholders and vendors to evaluate and integrate AI tools

Qualifications:

  • Bachelor’s degree in Computer Science, Engineering, Statistics, or related quantitative field
  • 5+ years of hands‑on experience developing and deploying ML systems in production
  • Time‑series models
  • Generative AI
  • NLP and/or LLMs
  • Python and ML Frameworks - TensorFlow, Pytorch, Scikit-learn
  • Strong engineering background with ability to debug, deploy, and maintain ML systems
  • Solid understanding of MLOps, model monitoring, CI/CD, and scalable architectures
  • Understanding ML Outcomes related to Data Quality and Governance


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