AI/ML Software Developer Specialist

AI/ML Software Developer Specialist
Location: Austin, TX (Hybrid)
Duration: 12-Month Contract (with potential extensions)

Position Overview

We are seeking an experienced AI/ML Software Developer Specialist to support and expand enterprise Artificial Intelligence initiatives. This role will be responsible for transforming existing proof-of-concept (POC) AI solutions into scalable, secure, and production-ready applications that support transportation engineering, infrastructure management, roadway asset detection, plan review automation, and digital delivery programs.

The selected candidate will work closely with engineering, data science, cloud, and DevOps teams to design, develop, deploy, and maintain advanced AI-powered applications accessible through enterprise web interfaces. The ideal candidate will possess a strong software engineering background combined with hands-on expertise in Machine Learning, Large Language Models (LLMs), Computer Vision, MLOps, cloud platforms, and modern DevOps practices.

Contract Term

• Initial Contract Term: One (1) Year
• Possible extension options available based on project needs and performance.

Key Responsibilities

< data-start="1237" data-end="1271">AI/ML Application Development

• Design, develop, and deploy production-grade AI/ML applications supporting engineering and infrastructure workflows.
• Convert experimental AI prototypes into scalable enterprise solutions.
• Develop secure web-based interfaces for AI-powered tools and services.
• Build APIs and backend services to support machine learning applications and integrations.

< data-start="1631" data-end="1670">Large Language Models (LLMs) & NLP

• Develop and deploy Retrieval-Augmented Generation (RAG) systems.
• Fine-tune and optimize transformer-based models including BERT, GPT, T5, and related architectures.
• Implement prompt engineering strategies and model evaluation frameworks.
• Deploy and manage open-source and non-frontier LLMs using platforms such as Hugging Face and Ollama.

< data-start="2019" data-end="2049">Computer Vision Solutions

• Build and deploy computer vision applications for asset detection, infrastructure monitoring, and image analysis.
• Develop object detection, image segmentation, and real-time inference systems.
• Utilize frameworks such as PyTorch, TensorFlow, OpenCV, and YOLO.

< data-start="2316" data-end="2355">MLOps & Model Lifecycle Management

• Design and maintain ML pipelines for training, testing, deployment, monitoring, and retraining.
• Implement model tracking, experiment management, and version control.
• Utilize MLOps tools including MLflow, Kubeflow, Airflow, Weights & Biases, and similar platforms.
• Optimize model performance through quantization, pruning, and knowledge distillation.

< data-start="2715" data-end="2754">Cloud & Infrastructure Engineering

• Deploy and manage AI/ML workloads across major cloud platforms including AWS, Azure, Google Cloud Platform, and OCI.
• Develop scalable infrastructure using containers and orchestration platforms.
• Support distributed training environments and large-scale model deployment.

< data-start="3015" data-end="3039">DevOps & Automation

• Build and maintain CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or similar tools.
• Automate infrastructure provisioning and deployment processes using Ansible and scripting tools.
• Implement monitoring, logging, and operational support processes.

< data-start="3308" data-end="3341">Data Engineering & Analytics

• Work with structured and unstructured datasets.
• Design and optimize SQL and NoSQL databases.
• Implement vector databases and embedding-based retrieval systems.
• Develop advanced feature engineering workflows and feature store integrations.

< data-start="3589" data-end="3620">Collaboration & Governance

• Collaborate with business stakeholders, engineers, data scientists, and technical teams.
• Ensure compliance with organizational security, governance, and data management standards.
• Document technical architectures, deployment procedures, and operational processes.

Required Qualifications

Cloud Platforms – 8+ Years

• Experience deploying and managing AI/ML workloads on AWS, Azure, Google Cloud Platform, or OCI.
• Experience with services such as Azure AI, AWS SageMaker/Bedrock, Google Cloud Platform Vertex AI, or OCI AI Services.

DevOps & Infrastructure – 8+ Years

• Docker
• Kubernetes
• Ansible
• CI/CD automation

Databases – 8+ Years

• PostgreSQL
• MySQL
• NoSQL databases
• Vector databases

Scripting & Automation – 8+ Years

• Advanced proficiency in Bash and PowerShell.

CI/CD Tools – 8+ Years

• Azure DevOps
• GitHub Actions
• Jenkins
• Similar pipeline technologies

Python Development – 3+ Years

• Production-level Python development experience.
• Strong software engineering and coding practices.

NLP & Large Language Models – 3+ Years

• Transformer architectures
• GPT, BERT, T5
• Prompt engineering
• Fine-tuning
• RAG implementations

Time Series Analytics – 3+ Years

• Forecasting models
• Anomaly detection systems
• Sequential data processing
• Real-time monitoring

Recommender Systems – 3+ Years

• Collaborative filtering
• Ranking systems
• Personalization engines
• Recommendation algorithms

MLOps Platforms – 3+ Years

• MLflow
• Kubeflow
• Airflow
• Weights & Biases

Distributed Training – 3+ Years

• Multi-GPU training
• Multi-node environments
• Data parallelism
• Large-scale model training

Computer Vision – 3+ Years

• PyTorch
• TensorFlow
• OpenCV
• YOLO
• Object detection and segmentation

Feature Engineering – 3+ Years

• Feature stores such as Feast or Tecton.
• Advanced feature engineering techniques.

Model Optimization – 3+ Years

• Quantization
• Pruning
• Knowledge distillation

Open-Source LLM Platforms – 3+ Years

• Ollama
• Hugging Face
• Other open-source AI ecosystems

Production AI/ML Systems – 2+ Years

• Experience building and deploying AI/ML solutions used by real-world users in production environments.

Preferred Qualifications

• Experience with Geographic Information Systems (GIS) and spatial analytics.
• Transportation, logistics, infrastructure, or smart-city industry experience.
• Computer Vision applications involving roadway, vehicular, or infrastructure datasets.
• Knowledge of public-sector compliance, governance, and security frameworks.
• Experience with Unreal Engine and digital twin technologies.
• Familiarity with Google Maps and Cesium APIs.
• Experience with Polygonflow Dash and related visualization platforms.