Machine Learning Solutions Architect

Summary

Join Gretel's team as a Senior/Staff/Principal Machine Learning Solutions Architect to help operationalize our product in enterprise customers' environments, drive go-to-market efforts, and contribute to applied science thought leadership.

Requirements

• 5+ years of experience in a technical customer-facing role serving Enterprise customers and showcasing a track record of successful technical sales scoping, design, and implementation
• 3+ years of experience working with modern machine learning frameworks and deep learning models, including fluency in Python, utilizing Colab or Jupyter notebooks, and working with open-source libraries, such as Pandas
• Experience working with data pipelines and orchestration / tooling for the modern data stack
• Previous hands on engineering experience in Data Engineering and MLOps
• Experience deploying ML models and required infrastructure set up, including Kubernetes (Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), and Azure Kubernetes Services (AKS)), containers, and CI/CD
• Exceptional presentation and communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences
• Ability to prioritize and manage multiple projects at once, across different customers with different use cases
• Willingness to travel occasionally (up to 20%) for customer meetings, conferences, and industry events, as needed
• Fluency in English is required; proficiency in additional languages is a plus

Responsibilities

• Build custom prototypes and product demos utilizing Colab/Jupyter notebooks and Python libraries that highlight end-to-end operationalized use cases of Gretel
• Lead and support customers in identifying use cases, scoping, and, partnering with the broader team to ensure the successful deployment of solutions tailored to meet their specific business use cases
• Be the voice of the customer, communicating back experimental results and empirical experience gained from the field and critical for our internal applied science research
• Proactively identify opportunities in our product based on trends identified across customer needs, and build solutions to address these emerging patterns
• Conduct and guide research in the field, working with our most pioneering customers to advance what is possible with our platform
• Lead technical discovery during the sales lifecycle to deeply understand prospects’ ML and engineering requirements
• Partner with the account teams to differentiate proposed approaches versus open source and competitive solutions
• Stay up-to-date with industry trends, best practices, and advancements in generative AI, data privacy, and cloud infrastructure
• Exhibit a customer-focused mindset by prioritizing client needs, fostering strong relationships, and delivering exceptional service to ensure customer satisfaction and success