Machine Learning Engineer

Deploy; Dallas (TX); On-site; USD 110,000 - 150,000; Full time

Benefits offered by this job

Competitive salary

Full benefits package

401k with company match

Gym reimbursement

Wellness reimbursement

Reading allowance

Catered breakfast

Job summary

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).

Responsibilities

• 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.

Skills

NLP/LLM

Python

DL Frameworks

AWS SageMaker

Docker

Kubernetes

CI/CD

Tools

Terraform

AWS CDK

MLFlow

Weights & Biases

ML Studio

Job description

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 rolein Dallas.

DEPLOY's client willhire 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 dynamicbusiness environment.

Our solutions attack one of the biggest business problems in existence today:

The Phone.

As a member of the Machine Learning (ML) Team you will:

• Design & Deploy ML Models:
• Build APIs & Pipelines:

  Construct APIs and automated pipelines that integrate real-time or batch data (e.g., calltranscripts) 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-basedML 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.

Our Ideal Candidates:

• 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/HuggingFace), 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 experimenttracking (MLFlow, W&B, ML Studio). A proactive, collaborative approach; eagerness to learn from senior engineers and improve bothML 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.

What's in It for You?

• Competitive salary package (immediate PTO).
• Full benefits package.
• Fidelity 401k with company match.
• Fun perks including a monthly gym reimbursement, a monthly wellness reimbursement, and amonthly reading allowance.
• Weekly catered breakfast, Employee of the Month rewards, regular company events, and bi-weekly happy hours.
• Opportunities for continued career growth within the organization.
• Fun and collaborative work environment.