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Visionary AI Solutions Architect
2 weeks ago
This role requires a highly skilled and versatile technologist to lead the design, development, and deployment of AI/ML and GenAI solutions across the business. The ideal candidate will be able to build end-to-end solutions from shaping business use cases to architecting pipelines, deploying models, and prototyping GenAI applications.
The AI & ML Solutions Engineer will work directly with internal stakeholders to deliver measurable value by automating, predicting, and optimizing across operations and supply chain. This is a high-impact role for someone who thrives at the intersection of engineering, data science, and business outcomes.
Key Responsibilities:- Own the AI lifecycle – Build, deploy, and maintain ML and GenAI solutions using Databricks, Azure, MLflow, and Snowflake.
- Experiment & deliver quickly – Run rapid proof-of-concepts to validate ideas, then scale what works into production.
- Automate for impact – Develop Python/R-based workflows, ML pipelines, and API endpoints to automate business processes.
- Lead solution design – Translate ambiguous business challenges into technical solutions, including MLOps infrastructure, model selection, and GenAI frameworks.
- Collaborate cross-functionally – Work closely with data engineers, architects, and business stakeholders to align solutions with strategic priorities.
- Enhance team capability – Share best practices and contribute to a culture of experimentation, continuous learning, and delivery.
- 3–5+ years of experience in data science, machine learning engineering, or full-stack AI solutioning
- Experience with end-to-end ML pipeline delivery from data prep and feature engineering to deployment and monitoring
- Strong Python skills and familiarity with Databricks, Snowflake, and cloud-native workflows
- Exposure to GenAI tools and frameworks, including building or experimenting with LLM-based applications
- Understanding of MLOps practices: model promotion, versioning, retraining, and feature stores
- Ability to work with stakeholders, frame business problems, and deliver outcomes—not just models
- Comfortable failing fast and iterating quickly based on feedback
- Consulting background or experience working directly with non-technical stakeholders
- Experience in manufacturing, supply chain, or industrial domains
- Familiarity with infrastructure-as-code and Git-based workflows
- Experience integrating AI/ML outputs into user-facing tools or internal workflows
- Medical insurance
- Vision insurance
- 401(k)
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