AI
American Family InsuranceDistinguished AI Architect
AI Architect developing data and AI architecture solutions for American Family Insurance. Leading integration design and participating in M&A discussions to achieve business outcomes.
About the role
- Partner with stakeholders to understand complex business challenges and translate them into actionable AI and data science problem statements
- Lead the identification and evaluation of data sources, ensuring quality and relevance for model development
- Perform in-depth exploratory data analysis (EDA) to uncover patterns, validate assumptions, and guide modeling decisions
- Design, develop, and optimize machine learning models, including experimenting with emerging AI techniques such as LLMs
- Collaborate with ML engineers and developers to operationalize models for scalable deployment and monitor performance
- Communicate insights and recommendations through clear, compelling visualizations and presentations to technical and non-technical audiences
- Contribute to defining best practices for experimentation, versioning, and reproducibility, and mentor junior team members where needed
- Stay proactive in learning and bringing innovative ideas from the latest advancements in AI, machine learning, and LLMs into the team’s work
Requirements
- Strong programming skills in Python, with the ability to write clean, modular, and testable code
- Solid experience with database technologies (SQL and/or NoSQL) and data manipulation at scale
- Good understanding of machine learning algorithms (supervised, unsupervised, and deep learning) and their practical applications
- Hands-on experience with ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, or similar
- Proficiency in data analysis using tools like pandas and NumPy
- Exposure to end-to-end ML workflows, from data pre-processing to model deployment (doesn’t need to be perfect, but familiarity is important)
- Awareness of MLOps practices (experiment tracking, model versioning, monitoring) is a plus
- Understanding of software engineering principles (version control with Git, testing, documentation; CI/CD and containerization like Docker are nice-to-have)
- Strong problem-solving skills, curiosity, and an experimental mindset — willing to try new approaches and learn emerging technologies
- Excellent collaboration and communication skills; comfortable working cross-functionally in a fast-moving environment
- Interest in cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML) and willingness to learn
- Familiarity or strong interest in LLMs, generative AI, or reinforcement learning — expertise not required, but enthusiasm is essential
Benefits
- Be a pioneer: Join a brand-new AI team and become one of the first engineers shaping its future
- Flexibility: Enjoy flexible working hours and a hybrid setup
- Global exposure: Work with stakeholders across multiple offices worldwide
- Cutting-edge tech: Experiment with emerging AI technologies, including LLMs and innovative approaches
- Collaboration: Partner with diverse teams and contribute to exciting, high-impact projects
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