(Senior to Principal) Machine Learning Solutions Architect

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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.