ML Engineer – NLP/LLM in Production (Dallas Office)

Deploy Alloy; Dallas (TX); On-site; USD 110,000 - 165,000; Full time

Benefits offered by this job

Immediate PTO

Full benefits

401k with company match

Gym reimbursement

Wellness reimbursement

Reading allowance

Catered breakfasts

Job summary

DEPLOY seeks a Mid Level Machine Learning Engineer for an in-office role in Dallas. You will design and deploy ML models, build APIs and pipelines, and implement MLOps practices across AWS, Azure, and containerized environments.

You will collaborate with data engineers and product teams, monitor model performance, and mentor junior engineers while advancing scalable AI solutions for automotive dealerships. Experience with NLP/LLM and transformer models is required.

Qualifications

• 3+ years in ML engineering with NLP/LLM experience.
• Hands-on with transformer models deployed in production (e.g., GPT, BERT).
• Strong Python and deep-learning frameworks; cloud ML experience (AWS SageMaker) and Docker.
• Familiarity with CI/CD (Azure), infrastructure-as-code (Terraform/CDK), and experiment tracking (MLFlow, Weights & Biases, ML Studio).
• Experience with AWS event-driven architectures (EventBridge, SQS) and real-time data pipelines.

Responsibilities

• Design and deploy ML models.
• Build APIs and pipelines that integrate real-time/batch data to power AI features.
• Implement MLOps, model versioning, CI/CD, containerization, and orchestration for reliable deployments.
• Utilize Terraform and AWS CDK to manage scalable cloud ML environments on AWS.
• Track experiments and metrics using MLFlow, Weights & Biases, or ML Studio.
• Monitor performance and optimize models for latency and scalability.
• Collaborate with data engineers and product teams to align solutions with business goals.
• Mentor junior engineers and promote ML engineering best practices.

Skills

NLP/LLM

Python

PyTorch

TensorFlow

HuggingFace

AWS SageMaker

Docker

Kubernetes

Azure CI/CD

Terraform

CDK

MLFlow

Weights & Biases

ML Studio

APIs & Pipelines

MLOps

Event-driven architectures

Tools

Docker

Kubernetes

Terraform

AWS CDK

Azure

SageMaker

EC2/Fargate

Prometheus

CloudWatch

Git

Job description

DEPLOY seeks a Mid Level Machine Learning Engineer for an in-office role in Dallas. You will design and deploy ML models, build APIs and pipelines, and implement MLOps practices across AWS, Azure, and containerized environments.

You will collaborate with data engineers and product teams, monitor model performance, and mentor junior engineers while advancing scalable AI solutions for automotive dealerships. Experience with NLP/LLM and transformer models is required.