Lead Machine Learning Engineer LLMs & Intelligent Workflow Systems

Job Title: Lead Machine Learning Engineer LLMs & Intelligent Workflow Systems

Location: Dallas, TX

Job Overview:

We are seeking a Lead Machine Learning Engineer with a proven track record of building and deploying production-grade machine learning systems and leading technical teams. This role focuses on the use of Large Language Models (LLMs) for advanced tasks such as data extraction, document processing, and intelligent workflow automation, rather than chatbot or conversational AI applications.

The ideal candidate brings both technical depth and leadership experience, with hands-on expertise in Python, modern ML frameworks, and LLM integration using providers like OpenAI, Anthropic, Cohere, or open-source models.

Key Responsibilities:

• Lead the design, development, and deployment of LLM-powered systems focused on information extraction, semantic understanding, and workflow automation
• Provide technical leadership across ML initiatives, guiding architecture, code quality, and best practices
• Integrate commercial and open-source LLMs (e.g., GPT, Claude, Mistral, LLaMA) into production pipelines
• Design and optimize prompt engineering strategies for use cases involving document summarization, classification, and data parsing
• Collaborate with data engineers, DevOps, and business stakeholders to embed ML models into operational workflows
• Define and monitor performance metrics and drive continuous improvement in accuracy, scalability, and cost-efficiency

Must-Have Qualifications:

• 3+ years in a technical leadership role, leading ML projects from concept to production
• Strong experience building and scaling machine learning systems in production environments
• Deep proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers
• Practical experience working with LLMs (e.g., OpenAI, Anthropic, Cohere, or open-source alternatives)
• Expertise in prompt engineering, data extraction, semantic parsing, or information retrieval
• Familiarity with MLOps tools and deployment techniques in cloud or containerized environments

Preferred Qualifications:

• Experience with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding-based search
• Knowledge of retrieval-augmented generation (RAG) architectures
• Exposure to document pipelines, workflow orchestration, or enterprise data systems

• Understanding of model evaluation, A/B testing, and observability in LLM-powered applications