Cradlepoint is looking for a self-directed and skilled Data Science with Python Developer to support software developers, database architects, data analysts, and AI/ML architects on various internal and external data science and AI/ML related initiatives and projects that run in cloud-based environments. You'll be comfortable supporting the data analytics and AI/ML needs of multiple teams, systems, and products, and will be responsible for integrating created solutions with the architecture used across the company and its customers.

What You Will Do: Key Responsibilities

Design, Architect, and Deliver AI/ML and/or Generative AI solutions hand-in-hand with business representatives, always considering the business ROI.
Create and maintain optimal data pipeline architecture for efficient data flow and processing.
Identify Data intensive and AI/ML Use Cases for different existing Managed Service accounts.
Prepare technical presentations, design documents, and demonstrations for customer presentations on Data strategy and AI/ML Solutions.
Prepare design and solution documents for both classic AI/ML and Generative AI-based solutions.
Assemble large, complex data sets that meet various functional and non-functional business requirements.
Design solutions that will keep data separated and secure across national boundaries through multiple data centers and strategic customers/partners, adhering to international security standards, organization, and customer security requirements.

What You Will Bring: Required Qualifications

Experience: 9 - 17 years of Telecom/IT experience in a Data-related Role with AI/ML and Data Science experience.
Python Development: At least 5 years of experience using Python for AI/ML and automation, with proficiency in commonly used data engineering and machine learning libraries (e.g., pandas, numpy, scikit-learn).
Data Pipeline Development: Experience with data pipeline development in technologies like Elastic (ELK) Stack, Hadoop, Spark, Kafka, etc.
Database Knowledge: Experience with Relational SQL and NoSQL databases, such as Postgres and Cassandra.
MLOps Experience: Hands-on experience with MLOps, including Model deployment and monitoring.
AI/ML Solutions: Proven experience in developing AI/ML Solutions for prediction, classification, and Natural Language Processing.
Generative AI Technologies: Working knowledge of Generative AI technologies like Large Language Models (LLMs), Agentic AI architecture, RAG (Retrieval-Augmented Generation), and other developing technologies.
Containerization: Hands-on experience with Kubernetes and containerization technologies (e.g., Docker).
Operating Systems & Scripting: Proficiency in Linux & Shell scripting knowledge.

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