Machine Learning Engineer

Title: Principal Machine Learning Engineer
Location: Dallas, TX (Onsite, 5 days/week)
Secondary: Chicago, IL or New York City, NY (also onsite)
Duration/Type: Contract
Client: System Integrator

Role Overview
Lead design and delivery of scalable ML/GenAI solutions for a large-scale Mail/communications platform, focusing on personalization, user insights, and production-grade ML systems.

Key Responsibilities

• Design, build, and deploy ML/GenAI models for personalization, recommendations, and user insights.
• Lead and mentor a small team of ML/Software Engineers; enforce high standards for code and architecture.
• Work with product, data, and platform teams to integrate ML features into Mail and broader ecosystem.
• Build data/ML pipelines and tools for large-scale data processing, experimentation, and observability.
• Run A/B tests and analyze impact; implement feedback loops and safe recovery paths for model failures.
• Balance cost, latency, and quality for production ML services.

Required Skills & Experience

• 8+ years in Machine Learning / Data Science / Applied ML Engineering.
• Strong programming in Python (preferred) and/or Java/C++.
• Hands-on with ML frameworks: TensorFlow, PyTorch, Scikit-learn.
• Experience with LLMs / Generative AI: prompt engineering, RAG, fine-tuning, LLMOps, agent frameworks, GPU optimization.
• Big data & ML infra: Spark, Hadoop, Airflow, MLFlow, feature stores, vector DBs, CI/CD for ML.
• Strong ML fundamentals: supervised/unsupervised learning, NLP, data mining, experimentation (A/B testing).
• Experience building production ML systems with monitoring, telemetry, and user feedback loops.
• Excellent communication; able to work closely with tech leads, architects, and cross-functional teams.