The Senior Software Developer Specialist will be responsible for advancing TX client AI initiatives by extending existing proof-of-concept (POC) solutions into scalable, production-ready web applications that directly support transportation engineering workflows. This role will focus on transforming early-stage AI models and prototypes—such as those supporting plan review automation, roadway asset detection, and digital delivery—into fully integrated applications accessible through secure, user-friendly web interfaces across the enterprise.
CANDIDATE SKILLS AND QUALIFICATIONS
| Minimum Requirements: Candidates that do not meet or exceed the minimum stated requirements (skills/experience) will be displayed to customers but may not be chosen for this opportunity. | ||
| Years | Required/Preferred | Experience |
| 8 | Required | 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). |
| 8 | Required | DevOps: Ansible, CI/CD, Docker and Kubernetes experience. |
| 8 | Required | Databases: SQL (PostgreSQL, MySQL) and NoSQL/vector databases. |
| 8 | Required | Scripting: Proficient in both Bash and PowerShell for automation. |
| 8 | Required | CI/CD Experience: Azure DevOps, GitHub Actions, Jenkins, or similar automation pipelines. |
| 3 | Required | Python: 3-5+ years production experience, this is your primary language. |
| 3 | Required | NLP/LLMs: Experience with transformers (BERT, GPT, T5), RAG systems, fine-tuning, prompt engineering, or building LLM applications. |
| 3 | Required | Time Series: Forecasting models, anomaly detection, sequential data modeling, or real-time monitoring systems. |
| 3 | Required | Recommender Systems: Collaborative filtering, ranking models, personalization engines, or content recommendations. |
| 3 | Required | MLOps Tools: Production experience with MLflow, Weights & Biases, Kubeflow, Airflow, or similar platforms. |
| 3 | Required | Distributed Training: Large-scale model training, multi-GPU/multi-node setups, efficient data parallelism. |
| 3 | Required | Computer Vision: Production CV experience with PyTorch/TensorFlow, OpenCV, YOLO, object detection, segmentation, or real-time inference. |
| 3 | Required | Feature stores (Feast, Tecton) or advanced feature engineering. |
| 3 | Required | Model optimization: quantization, pruning, knowledge distillation. |
| 3 | Required | LLM Models: Ollama, Huggingface, or other non-frontier models |
| 2 | Required | AI/ML Production: Built and deployed 2-3+ ML models serving real users, not just experiments. |
| 1 | Preferred | Experience with Geospatial Information Systems (GIS) and analyzing spatial data. |
| 1 | Preferred | Prior experience in the transportation, logistics, or smart city sectors. |
| 1 | Preferred | Background in Computer Vision (object detection, image segmentation) applied to infrastructure or vehicular data. |
| 1 | Preferred | Familiarity with public sector data compliance, security, and governance standards. |
| 1 | Preferred | Experience with the Unreal gaming engine and real world digital twinning |
| 1 | Preferred | Experience with Google Maps Cesium API |
| 1 | Preferred | Experience with Polygonflow Dash and its capabilities |
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