Job Title: Ai Engineer (GenAI & RAG)
Onsite Location: Grand Rapids, MI
Target senior AI/ML architects with strong experience in GenAI, LLMs, and RAG, along with proven expertise in AI system design and architecture strategy. Prioritize candidates with hands-on MLOps, cloud AI platforms, data engineering, and scalable AI solution architecture experience.
Overview / Summary
We are seeking an experienced AI/ML Solutions Architect with 10+ years of software engineering and/or data science experience, including 3–5+ years in AI/ML architecture roles. The role is responsible for defining AI/ML architecture strategy, designing end-to-end AI solutions, and leading architecture initiatives across Generative AI, LLMs, machine learning, and data platforms.
Key Responsibilities
- Define and drive the AI/ML architecture strategy across the organization.
- Design end-to-end AI solutions.
- Lead architecture for Generative AI, LLMs, and traditional machine learning models.
- Design scalable data pipelines and real-time/streaming architectures.
- Establish MLOps practices for model versioning, deployment, and monitoring.
- Collaborate with AI and Data teams to define and promote best practices in AI system design.
- Evaluate emerging AI technologies and define adoption strategies.
Required Qualifications
- 10+ years of experience in software engineering and/or data science.
- 3–5+ years of experience in AI/ML architecture roles.
- Strong experience in AI/ML architecture and system design.
- Strong experience with RAG (Retrieval-Augmented Generation).
- Expertise in machine learning, deep learning, and NLP.
- Hands-on experience with LLMs and Generative AI (GPT, Llama, etc.).
- Proficiency in Python and frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Experience with cloud platforms including AWS, Azure, and Google Cloud Platform AI services.
- Strong knowledge of data engineering, including ETL pipelines, data lakes, and streaming architectures.
- Experience with MLOps tools such as MLflow, Kubeflow, SageMaker, and Vertex AI.
- Understanding of APIs, microservices, and distributed systems.
- Knowledge of vector databases.
- Experience with DevOps/CI-CD pipelines for machine learning.
- Exposure to AI governance and compliance.
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