Machine Learning Engineer

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.

DEPLOYs 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:

Develop fine-tune and deploy NLP and LLM-driven models using frameworks likePyTorch TensorFlow or Hugging Face ensuring they are robust scalable andproduction-ready.

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 practicesmodel versioning automated CI/CD pipelines (Azure)containerization (Docker) orchestration (Kubernetes)to ensure reliable repeatabledeployments.

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 andoptimize models for latency accuracy and scalability.

Cross-Functional Collaboration:
Partner with data engineers product managers and senior ML engineers to align technicalsolutions with business goals.
Contribute to evolving data pipelines and guide improvements based on user feedback andperformance & Best Practices:
Participate in code reviews pair programming and technical discussions.
Serve as a mentor to junior team members sharing best practices in ML engineeringMLOps 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) tomanage 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.

Whats 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.