AI Development Engineer

AI Development Engineer (Contract Position)

Location:

Dallas, TX (Onsite)

Type:
Contract Position

We are seeking a highly skilled AI Development Engineer with strong expertise in telecom domains (4G/5G and OSS/BSS) and advanced AI technologies. The candidate will design and implement AI‑driven solutions for test automation, leveraging LLMs and Retrieval‑Augmented Generation (RAG) to enhance efficiency and accuracy in telecom testing environments.

• Bachelor’s degree in Computer Science, Information Technology, AI/ML, Data Science, or related field.
• Certifications in AI/ML technologies, LLM development, or telecom protocols are a plus.

• Develop and integrate AI models for telecom test automation using Python, LLMs, and RAG techniques.
• Short‑train and fine‑tune AI engines with existing Verizon test scripts, scenarios, and domain‑specific data.
• Generate Robot Framework scripts using GenAI to automate test case creation and execution.
• Integrate AI solutions with existing test platforms and tools for seamless automation workflows.
• Perform test execution and Iris View log analysis, generating summaries and actionable insights using AI‑driven approaches.
• Collaborate with QA, Dev Ops, and network engineering teams to embed AI capabilities into CI/CD pipelines.
• Design and implement data pipelines for training and inference, ensuring data quality and compliance.

• 3+ years of experience in AI development and telecom domains (4G/5G and OSS/BSS).
• Strong proficiency in Python and AI frameworks (Tensor Flow, PyTorch, Hugging Face).
• Hands‑on experience with Large Language Models (LLMs) and Retrieval‑Augmented Generation (RAG).
• Familiarity with GenAI‑based script generation and automation frameworks like Robot Framework.
• Solid understanding of telecom protocols (LTE, 5G , Diameter, SIP, etc.).
• Experience in log analysis and summarization using AI/NLP techniques.
• Strong problem‑solving and debugging skills for AI‑driven automation solutions.
• Knowledge of MLOps practices and deployment of AI models in production.
• Experience with vector databases (e.g., Pinecone, Weaviate) for RAG implementations.
• Familiarity with API development and integration for AI services.
• Exposure to cloud platforms (AWS, Azure, Google Cloud Platform) for AI model hosting.
• Understanding of CI/CD pipelines and integration with AI workflows.
• Knowledge of data preprocessing, feature engineering, and prompt engineering for LLMs.