Senior Software Developer (Contractor) RITM1788244

RESPEC•Austin, TX

7d•Hybrid

About The Position

RESPEC is seeking an experienced Software Developer Specialist to support a major transportation technology initiative for our government client in Austin, Texas. This role focuses on designing, developing, deploying, and optimizing advanced Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, and cloud-based solutions that support large-scale operational and business objectives. The ideal candidate will bring deep expertise across AI/ML engineering, cloud platforms, MLOps, DevOps, and production-grade software development while collaborating with technical and business stakeholders in a highly visible public-sector environment.

Requirements

8+ Years of Experience Required: Cloud Platforms & AI Infrastructure (AWS, Microsoft Azure, Google Cloud Platform (GCP), or Oracle Cloud Infrastructure (OCI))
Deploying and managing machine learning workloads in cloud environments
Utilizing AI/ML services across major cloud providers
DevOps & Platform Engineering (Ansible, Docker, Kubernetes, CI/CD implementation and automation)
Database Technologies (SQL databases including PostgreSQL and MySQL, NoSQL databases, Vector databases)
Automation & Scripting (Bash scripting, PowerShell scripting)
CI/CD Tools (Azure DevOps, GitHub Actions, Jenkins, Comparable enterprise CI/CD platforms)
3+ Years of Experience Required: Python Development (Production-level Python application development, Building scalable backend and AI-driven solutions)
Natural Language Processing & Large Language Models (Transformer architectures, Retrieval-Augmented Generation (RAG), Fine-tuning models, Prompt engineering, LLM application development)
Time Series Analytics (Forecasting, Sequential modeling, Anomaly detection, Real-time monitoring systems)
Recommendation Systems (Collaborative filtering, Ranking algorithms, Personalization engines, Content recommendation platforms)
MLOps (MLflow, Weights & Biases, Kubeflow, Apache Airflow, Similar MLOps platforms)
Distributed AI Training (Multi-GPU environments, Multi-node training, Data parallelism, Large-scale model training)
Computer Vision (PyTorch, TensorFlow, OpenCV, YOLO, Object detection, Image segmentation, Real-time inference systems)
Feature Engineering (Feature stores such as Feast or Tecton, Advanced feature engineering methodologies)
Model Optimization (Quantization, Pruning, Knowledge distillation)
Alternative/Open-Source LLM Platforms (Ollama, Hugging Face, Other non-frontier/open-source model ecosystems)
2+ Years of Experience Required: Production AI/ML Delivery (Demonstrated experience building and deploying at least 2–3 machine learning models used by real-world users in production environments)

Nice To Haves

GIS and spatial data analysis experience
Transportation, logistics, or smart-city technology experience
Computer vision applications involving infrastructure, roadway, or vehicle-related data
Public-sector data governance, compliance, and security experience
Unreal Engine experience
Digital twin implementation experience
Google Maps Cesium API experience
Polygonflow Dash experience

Responsibilities

Design, develop, test, and deploy scalable AI/ML solutions in cloud environments.
Build and maintain production-grade machine learning pipelines and model deployment frameworks.
Develop applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and transformer-based architectures.
Create and optimize NLP solutions, recommendation systems, forecasting models, and anomaly detection systems.
Design and implement computer vision solutions for real-time and large-scale data processing.
Develop and maintain MLOps workflows, model monitoring, and automated retraining processes.
Build and manage CI/CD pipelines supporting AI and software delivery.
Containerize and deploy applications using Docker and Kubernetes.
Collaborate with cross-functional teams to gather requirements and translate business needs into technical solutions.
Optimize model performance through quantization, pruning, distillation, and distributed training techniques.
Work with structured, unstructured, vector, and spatial datasets to support analytics and predictive modeling initiatives.
Document solutions, architectures, and deployment processes according to client standards.
Participate in technical reviews, troubleshooting, and ongoing operational support.