ML Engineer – NLP & MLOps for Real-Time AI

deploy; Dallas (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

DEPLOY, based in Dallas, seeks a mid-level Machine Learning Engineer for an in-office role to build, deploy, and monitor ML solutions for call tracking, CRM integration, and AI features for automotive dealership clients.

You will design and deploy ML models, build APIs and pipelines, manage MLOps, and collaborate with data engineers and product managers to deliver scalable, reliable systems with a focus on latency and accuracy.

Qualifications

• 3+ years in ML engineering with hands-on NLP/LLM expertise and production deployment experience.
• Strong Python development skills with deep learning frameworks (PyTorch/TensorFlow).
• Experience with cloud ML (AWS SageMaker, EC2) and containerization (Docker) with orchestration (Kubernetes).
• Familiarity with CI/CD and infrastructure-as-code (Terraform, CDK).
• Understanding of model monitoring, experiment tracking (MLFlow, Weights & Biases) and data security best practices.

Responsibilities

• Design, train, and deploy ML models for real-time or batch data.
• Build APIs and data pipelines to power AI features in products.
• Implement MLOps practices and CI/CD pipelines for reliable deployments (Azure).
• Maintain scalable ML environments on AWS (SageMaker, EC2) and use Terraform/CDK for infra.
• Track experiments and metrics using MLFlow, Weights & Biases, or ML Studio.
• Collaborate with data engineers, PMs, and senior ML engineers to align solutions with business goals.

Skills

NLP/LLM

Python

Deep Learning

CI/CD

Collaboration

Tools

PyTorch

TensorFlow

HuggingFace

Docker

Kubernetes

AWS SageMaker

Terraform

CDK

MLFlow

Weights & Biases

ML Studio

Azure

Job description

DEPLOY, based in Dallas, seeks a mid-level Machine Learning Engineer for an in-office role to build, deploy, and monitor ML solutions for call tracking, CRM integration, and AI features for automotive dealership clients.

You will design and deploy ML models, build APIs and pipelines, manage MLOps, and collaborate with data engineers and product managers to deliver scalable, reliable systems with a focus on latency and accuracy.