Mid-Senior GenAI / Machine Learning Engineer (Telecom Domain)

We''re looking for a Mid-Senior GenAI/Machine Learning Engineer with hands-on AI/ML experience and a strong background in telecommunications. You''ll design and deploy Generative AI applications, work with Large Language Models (LLMs), and build ML solutions addressing real telecom use cases — customer support, network optimization, operations, and predictive analytics.

What You''ll Do

• Design, develop, and deploy Generative AI and Machine Learning solutions for telecom business use cases
• Build AI-powered applications using LLMs, Retrieval-Augmented Generation (RAG), and AI agents
• Develop scalable ML pipelines for model training, evaluation, deployment, and monitoring
• Partner with business stakeholders, data engineers, and solution architects to translate requirements into AI solutions
• Fine-tune foundation models and optimize prompt engineering strategies
• Work with structured and unstructured telecom data to build predictive and conversational AI solutions
• Integrate AI/ML models with cloud platforms and enterprise applications
• Ensure AI solutions follow security, governance, and responsible AI best practices

What You Bring

• 5–8 years of experience in Machine Learning, Data Science, or Artificial Intelligence
• 2+ years of hands-on experience with Generative AI technologies
• Strong Python skills and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Experience working with LLMs (OpenAI, Llama, Claude, Gemini, etc.)
• Hands-on experience with RAG architectures, vector databases (Pinecone, FAISS, Chroma, or Milvus), and prompt engineering
• Experience with AI orchestration frameworks such as LangChain or LlamaIndex
• Solid understanding of NLP, embeddings, model evaluation, and AI application development
• Experience with Azure AI, Azure OpenAI, AWS Bedrock, or Google Vertex AI
• Working knowledge of REST APIs, Docker, Git, and CI/CD pipelines
• Telecom domain experience required

Important

• Candidates must be local to Dallas, TX or willing to be onsite 3 days/week
• Final round interview is mandatory onsite — candidates unable to attend in person will not be considered