
Python AI/ML Specialist
14 hours ago
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.
- Experience: 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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