Machine Learning Engineer

Mid-Level Machine Learning Engineer (MLOps Focus)Location: Remote (Texas) Team: Machine LearningAbout the RoleWe build AI-driven products that bring machine learning models into production at scale, supporting tens of thousands of inferences daily across dozens of models. Our systems handle complex conversational AI use cases — including intent extraction, sentiment analysis, and voice recognition — for real-world business applications.This role is weighted more heavily toward MLOps and infrastructure than toward model research or development. If you enjoy building the pipelines, tooling, and infrastructure that get models into production reliably — more than tuning model architecture from scratch — this is a strong fit.What You'll DoDeploy & Maintain ML Models: Take NLP and LLM-driven models (built with PyTorch, TensorFlow, or Hugging Face) and get them into robust, scalable, production-ready systems.Build APIs & Pipelines: Construct APIs and automated pipelines that integrate real-time or batch data (e.g., call transcripts) to power conversational AI features.MLOps & Model Monitoring: Implement MLOps best practices — model versioning, CI/CD pipelines (Azure), containerization (Docker), and orchestration (Kubernetes) — for reliable, repeatable deployments.Infrastructure as Code: Use Terraform or AWS CDK to maintain scalable, cloud-based ML environments on AWS (SageMaker, EC2/Fargate).Experiment Tracking & Performance: Track experiments and metrics using MLFlow, Weights & Biases, or ML Studio. Monitor performance (Prometheus, CloudWatch) and troubleshoot for latency, accuracy, and scalability.Cross-Functional Collaboration: Partner with data engineers, product managers, and senior ML engineers to align technical work with business goals.Mentorship: Participate in code reviews and pair programming; share MLOps and deployment best practices with junior team members.What You Need3+ years of ML engineering experience, with hands-on NLP/LLM work — ideally deploying transformer-based models (e.g., GPT, BERT) in production.Strong Python skills, with experience in a deep learning framework (PyTorch, TensorFlow, or Hugging Face).Working knowledge of Kubernetes and Docker for containerized deployment and orchestration.Experience with Terraform or AWS CDK for infrastructure-as-code.Familiarity with cloud ML infrastructure (AWS SageMaker, EC2) and CI/CD pipelines (Azure).Experience with experiment tracking tools (MLFlow, W&B, or ML Studio).A proactive, collaborative mindset and eagerness to grow MLOps and ML engineering skills.Nice to HaveExperience with AWS event-driven/streaming architectures (EventBridge, SQS) for large-scale, real-time data pipelines.Understanding of security, compliance, and reliability practices in ML deployments.Prior work with voice recognition, sentiment analysis, or conversational AI frameworks.What's In It For YouCompetitive compensation and benefits401(k) with company matchWellness and professional development stipendsDirect mentorship from senior engineers, with a defined path for career growthA hands-on environment where your infrastructure and deployment work directly powers real-world AI products at scale