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Tier-1 employer, metro location, popular ML title, and broad required skills increase competition.
Core ML and data engineering skills transfer well, though networking-specific systems slightly reduce portability.
Explicit 7+ years plus many mandatory ML, data engineering, and cloud skills make filters stringent.
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Develop and optimize scalable data pipelines, workflows, and Feature Stores for AI-driven solutions in networking support and services.
Implement AI/ML techniques for anomaly detection, sensitive data redaction, text extraction and summarization, ensuring data accuracy, security, and governance.
Integrate multiple data sources using AWS S3 and Snowflake, automate observability of data pipelines, and build AI applications interacting with MCP servers.
7+ years of data analysis and AI/ML engineering experience.
Bachelor’s degree in Computer Science, Statistics, Informatics, Information Systems, or related quantitative field.
Proficiency with Python, PySpark, SQL, RDBMS, web crawling, API data extraction (e.g., Salesforce API).
Experience with AI/ML techniques including Generative AI, LLMs, prompt engineering, NLP, vector databases, plus knowledge of SQL Data Warehouses (preferably Snowflake) and AWS services (S3, Glue, EMR, etc.).
Experienced in designing and managing end-to-end data pipelines for semi-structured and unstructured data with focus on AI/ML integration and scalability.
Comfortable working in hybrid environment collaborating cross-functionally, handling sensitive data ethically, and executing automation and observability solutions.
Strong technical skills with ability to innovate independently and advance digital-first AI-driven tooling in a networking support context.