Machine Learning Engineer (Mid-Level)

Deploy•Dallas, TX

10d•Onsite

About The Position

DEPLOY has been retained by a Dallas, Texas based firm that provides unique SaaS products to automotive dealerships across the United States. DEPLOY is a Mid Level Machine Learning Engineer for an in office role in Dallas. DEPLOY's client will hire smart and ambitious doers and set them loose in an exciting and complex technology business where they will build, sell, and deploy call tracking, CRM integration and Artificial Intelligence solutions in a dynamic business environment. Our solutions attack one of the biggest business problems in existence today: The Phone.

Requirements

3+ years in ML engineering, with hands-on NLP/LLM expertise, ideally deploying transformer-based models (e.g., GPT, BERT) in production.
Strong Python skills and experience with deep learning frameworks (PyTorch/TensorFlow/Hugging Face), plus familiarity with cloud-based ML (AWS SageMaker, EC2), containerization (Docker), and orchestration (Kubernetes).
Working knowledge of CI/CD (Azure), infrastructure-as-code (Terraform/CDK), and experiment tracking (MLFlow, W&B, ML Studio).
A proactive, collaborative approach; eagerness to learn from senior engineers and improve both ML and MLOps skill sets.
Experience with AWS event-driven and streaming architectures (e.g., EventBridge, SQS) to manage large-scale, real-time data handling and ingestion pipelines.
Understanding of security, compliance, and reliability best practices in ML deployments.
Prior work with voice recognition, sentiment analysis, or conversational AI frameworks.

Responsibilities

Design & Deploy ML Models: Develop, fine-tune, and deploy NLP and LLM-driven models using frameworks like PyTorch, TensorFlow, or Hugging Face, ensuring they are robust, scalable, and production-ready.
Build APIs & Pipelines: Construct APIs and automated pipelines that integrate real-time or batch data (e.g., call transcripts) to power conversational AI features in our products.
MLOps & Model Monitoring: Implement MLOps best practices—model versioning, automated CI/CD pipelines (Azure), containerization (Docker), orchestration (Kubernetes)—to ensure reliable, repeatable deployments. Employ infrastructure-as-code (Terraform, AWS CDK) to maintain scalable, cloud-based ML environments on AWS (SageMaker, EC2/Fargate).
Experiment Tracking & Performance: Track experiments, artifacts, and metrics using MLFlow, Weights & Biases, or ML Studio. Continuously monitor performance (Prometheus, CloudWatch), troubleshoot issues, and optimize models for latency, accuracy, and scalability.
Cross-Functional Collaboration: Partner with data engineers, product managers, and senior ML engineers to align technical solutions with business goals. Contribute to evolving data pipelines and guide improvements based on user feedback and performance metrics.
Mentorship & Best Practices: Participate in code reviews, pair programming, and technical discussions. Serve as a mentor to junior team members, sharing best practices in ML engineering, MLOps, and model lifecycle management.