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
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
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
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Qualifications:
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- 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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