Dallas ML Engineer — NLP & MLOps Focus

Deploy; Dallas (TX); Sur place; USD 110 000 - 150 000; Plein temps

Avantages offerts par ce poste

Competitive salary

Full benefits package

401k with company match

Gym reimbursement

Wellness reimbursement

Reading allowance

Catered breakfast

Résumé du poste

DEPLOY is seeking a Mid Level Machine Learning Engineer for an in-office role in Dallas. You will design, deploy, and monitor ML models that power call tracking, CRM integrations, and conversational AI features across automotive dealership solutions.

You will build APIs and data pipelines, utilize containerization with Docker, and manage MLOps practices using AWS, Terraform, and CI/CD to ensure scalable, reliable deployments.

Qualifications

• 3+ years in ML engineering with hands-on NLP/LLM expertise, preferably deploying transformer-based models in production.
• Strong Python skills and experience with deep learning frameworks (PyTorch, TensorFlow, HuggingFace), plus cloud ML (AWS SageMaker, EC2).
• Proficiency with Docker, Kubernetes, and CI/CD (Azure or AWS) for reliable deployments.
• Experience with infrastructure-as-code (Terraform, CDK) and experiment tracking tools (MLFlow, Weights & Biases, ML Studio).

Responsabilités

• Design and deploy ML models in production environments.
• Build APIs and data pipelines to power conversational AI features.
• Implement MLOps, model monitoring, and CI/CD pipelines.
• Maintain scalable cloud ML environments on AWS (SageMaker, EC2/Fargate).
• Track experiments and metrics using ML tooling; optimize for latency and accuracy.
• Collaborate with data engineers and product teams to align solutions with goals.

Connaissances

NLP/LLM

Python

DL Frameworks

AWS SageMaker

Docker

Kubernetes

CI/CD

Outils

Terraform

AWS CDK

MLFlow

Weights & Biases

ML Studio

Description du poste

DEPLOY is seeking a Mid Level Machine Learning Engineer for an in-office role in Dallas. You will design, deploy, and monitor ML models that power call tracking, CRM integrations, and conversational AI features across automotive dealership solutions.

You will build APIs and data pipelines, utilize containerization with Docker, and manage MLOps practices using AWS, Terraform, and CI/CD to ensure scalable, reliable deployments.