DevOps Engineer

Robotics Technologies LLC
Atlanta, United States of America
21 days ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Junior

Job location

Atlanta, United States of America

Tech stack

A/B testing
Artificial Intelligence
Amazon Web Services (AWS)
Computer Vision
Azure
Bash
Command-Line Interface
Cloud Computing
Program Optimization
Databases
Continuous Integration
DevOps
Github
IT Management
Python
PostgreSQL
Machine Learning
MySQL
NoSQL
Object Detection
OpenCV
Open Source Technology
Powershell
TensorFlow
SQL Databases
Data Streaming
Data Processing
Scripting (Bash/Python/Go/Ruby)
Google Cloud Platform
Enterprise Software Applications
Feature Engineering
PyTorch
Delivery Pipeline
AI Platforms
Kafka
Machine Learning Operations
Software Coding
Oracle Cloud Infrastructure
Automation Anywhere
Docker
Jenkins

Job description

The Artificial Intelligence (AI) engineer will develop AI/ML proof-of-concept demonstrations and build new AI/ML solutions that scale with pipelines and workflows. The role will be embedded within the Traffic Technology team with the goal to develop AI/ML for Operational Technology that improves the safety and operations of the TxDOT roadway system. The AI engineer will work across teams such as Traffic Technology, ITD AI team, and TRF to gather business requirements, develop AI software, and demonstrate successful solutions to end-users.

Core Responsibilities:

  • Gather and document AI solution requirements from business stakeholders.
  • Develop AI proof-of-concepts and transition successful prototypes into production systems.
  • Design and implement scalable AI pipelines for enterprise applications.
  • Train, fine-tune, and validate AI/ML models for optimal performance.
  • Write clean, efficient software code and scripts for AI workflows.
  • Conduct rigorous testing and quality assurance of AI models and outputs.
  • Ensure compliance with organizational IT governance, security, and audit standards.

Stakeholder Engagement & Communication

  • Act as liaison between Traffic Technology team, business stakeholders, and automation developers.
  • Facilitate requirements gathering and ensure clarity in AI solution design.
  • Communicate progress, risks, and issues to project sponsors and leadership teams.

Delivery Excellence & Governance

  • Ensure automation projects comply with TxDOT's IT governance, security, and audit requirements.
  • Promote reusable components and standardized AI development practices.
  • Conduct post-implementation reviews to capture lessons learned and improve delivery methods.

Team Coordination & Support

  • Collaborate with data engineers, business analysts, and infrastructure teams.
  • Provide guidance on AI best practices and assist in troubleshooting.
  • Support knowledge sharing and continuous improvement within the team.

Requirements

  • Python - 1-3+ years production experience, this is your primary language
  • AI/ML Production - Built and deployed 1-3+ ML models serving real users, not just experiments
  • Cloud Platforms - Experience with AWS, Azure, GCP, or OCI for deploying and managing ML workloads. We leverage AI/ML tools across all major cloud providers (Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, OCI AI Services)
  • DevOps - Docker and Kubernetes experience
  • Databases - SQL (PostgreSQL, MySQL) and NoSQL/vector databases
  • Scripting - Proficient in both Bash and PowerShell for automation
  • Command Line Interface (CLI) - 1-3+ years production experience working in CLI terminal.

Preferred Skills and Qualifications

  • CI/CD Experience: Azure DevOps, GitHub Actions, Jenkins, or similar automation pipelines
  • Computer Vision: Production CV experience with PyTorch/TensorFlow, OpenCV, object detection, segmentation, or real-time inference
  • Additional Languages: Go or Rust experience for performance-critical components
  • Feature stores (Feast, Tecton) or advanced feature engineering
  • Model optimization: quantization, pruning, knowledge distillation
  • Edge deployment or resource-constrained model deployment
  • Experiment frameworks for A/B testing ML models
  • Contributions to open-source ML projects
  • Real-time streaming data processing (Kafka, Kinesis)

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