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Tier-1 brand, metro location, and broad multi-skill ML/architecture requirements increase applicant density.
Specialized ML/cloud architecture and enterprise pre/post-sales experience reduce cross-industry transferability.
Mandatory 10+ years plus deep ML, MLOps, cloud and hands-on technical requirements.
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Own technical expertise and solution design for AI/ML workloads on Snowflake, including building and deploying ML pipelines using Snowflake features and ecosystem partner tools.
Create hands-on POCs with SQL, Python, and APIs to demonstrate implementations and best practices for GenAI and ML workloads.
Work closely with customer teams and System Integrators, provide technical guidance, ensure knowledge transfer, and collaborate with internal teams to improve products and marketing.
Minimum 10 years of experience in pre-sales or post-sales technical roles working with customers.
University degree in computer science, engineering, mathematics, or related field, or equivalent experience.
Proficiency with SQL and at least one of Python, R, Java, or Scala scripting languages.
Ability and willingness to travel approximately 25% of the time for on-site customer engagements.
Deep understanding of the full Data Science lifecycle and MLOps, including feature engineering, model development, deployment, and management.
Experienced with at least one public cloud platform (AWS, Azure, or GCP) and one Data Science tool (e.g., Sagemaker, AzureML, Vertex).
Hands-on experience with Large Language Models, Retrieval and Agentic frameworks, and ML libraries such as Pandas, PyTorch, TensorFlow, or SciKit-Learn.