Machine Learning Engineer (Mid-Level)

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 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.