Vitaver & Associates, Inc
14358 – AI/Machine Learning Engineer (onsite) – Austin, TX
Start Date:
ASAP
Type:
Temporary Project
Estimated Duration:
12+ months with possible extensions
Work Setting:
Onsite. Working remotely is accepted in accordance with TxDOT’s policies. The resource must be in the office a minimum of four days a week, or as approved by TxDOT
Required
Availability to work 100% of the time at the Client’s site in
Austin, TX
(required);
Experience with Python production (8+ years)
Experience with AI/ML Production - Built and deployed 2-3+ ML models serving real users, not just experiments (8+ years)
Experience with AWS, Azure, GCP, or OCI for deploying and managing ML workloads (8+ years)
Experience with Docker and Kubernetes (8+ years)
Experience with Databases - SQL (PostgreSQL, MySQL) and NoSQL/vector databases (8+ years)
Experience with scripting in both Bash and PowerShell for automation (8+ years)
Experience with transformers (BERT, GPT, T5), RAG systems, fine-tuning, prompt engineering, or building LLM applications
Experience with MLflow, Weights & Biases, Kubeflow, Airflow, or similar platforms
Preferred
Experience with CI/CD such as Azure DevOps, GitHub Actions, Jenkins, or similar automation pipelines
Experience with PyTorch/TensorFlow, OpenCV, object detection, segmentation, or real-time inference
Experience for performance-critical components (Go or Rust)
Experience with feature stores (Feast, Tecton) or advanced feature engineering
Experience with model optimization: quantization, pruning, knowledge distillation
Experience with edge deployment or resource-constrained model deployment
Experience with frameworks for A/B testing ML models
Experience with open-source ML projects
Experience with real-time streaming data processing (Kafka, Kinesis)
Responsibilities
Design, build, and deploy end‑to‑end AI/ML systems from initial concept through production, ensuring models serve real users at scale and comply with TxDOT’s governance and SDLC standards.
Develop scalable ML pipelines and data workflows using Python, cloud‑native services (Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, OCI AI Services), and modern MLOps tooling such as MLflow, Kubeflow, Airflow, or Weights & Biases.
Implement and maintain production‑grade infrastructure for model training, deployment, monitoring, and distributed large‑scale training across multi‑GPU or multi‑node environments.
Engineer solutions leveraging advanced ML domains including NLP/LLMs (transformers, RAG, fine‑tuning), time‑series forecasting, anomaly detection, recommender systems, and vector/NoSQL database integrations.
Develop DevOps‑aligned automation and containerized environments using Docker, Kubernetes, Bash, and PowerShell to support reliable CI/CD, reproducibility, and cloud‑based ML workload orchestration.
Create internal tools, frameworks, and CLI‑first utilities that improve team efficiency, accelerate experimentation, and support greenfield AI initiatives across TxDOT.
Collaborate with cross‑functional teams to translate ambiguous requirements into working AI solutions, providing technical leadership, identifying risks, ensuring compliance, and guiding the adoption of standardized AI governance practices.
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Start Date:
ASAP
Type:
Temporary Project
Estimated Duration:
12+ months with possible extensions
Work Setting:
Onsite. Working remotely is accepted in accordance with TxDOT’s policies. The resource must be in the office a minimum of four days a week, or as approved by TxDOT
Required
Availability to work 100% of the time at the Client’s site in
Austin, TX
(required);
Experience with Python production (8+ years)
Experience with AI/ML Production - Built and deployed 2-3+ ML models serving real users, not just experiments (8+ years)
Experience with AWS, Azure, GCP, or OCI for deploying and managing ML workloads (8+ years)
Experience with Docker and Kubernetes (8+ years)
Experience with Databases - SQL (PostgreSQL, MySQL) and NoSQL/vector databases (8+ years)
Experience with scripting in both Bash and PowerShell for automation (8+ years)
Experience with transformers (BERT, GPT, T5), RAG systems, fine-tuning, prompt engineering, or building LLM applications
Experience with MLflow, Weights & Biases, Kubeflow, Airflow, or similar platforms
Preferred
Experience with CI/CD such as Azure DevOps, GitHub Actions, Jenkins, or similar automation pipelines
Experience with PyTorch/TensorFlow, OpenCV, object detection, segmentation, or real-time inference
Experience for performance-critical components (Go or Rust)
Experience with feature stores (Feast, Tecton) or advanced feature engineering
Experience with model optimization: quantization, pruning, knowledge distillation
Experience with edge deployment or resource-constrained model deployment
Experience with frameworks for A/B testing ML models
Experience with open-source ML projects
Experience with real-time streaming data processing (Kafka, Kinesis)
Responsibilities
Design, build, and deploy end‑to‑end AI/ML systems from initial concept through production, ensuring models serve real users at scale and comply with TxDOT’s governance and SDLC standards.
Develop scalable ML pipelines and data workflows using Python, cloud‑native services (Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, OCI AI Services), and modern MLOps tooling such as MLflow, Kubeflow, Airflow, or Weights & Biases.
Implement and maintain production‑grade infrastructure for model training, deployment, monitoring, and distributed large‑scale training across multi‑GPU or multi‑node environments.
Engineer solutions leveraging advanced ML domains including NLP/LLMs (transformers, RAG, fine‑tuning), time‑series forecasting, anomaly detection, recommender systems, and vector/NoSQL database integrations.
Develop DevOps‑aligned automation and containerized environments using Docker, Kubernetes, Bash, and PowerShell to support reliable CI/CD, reproducibility, and cloud‑based ML workload orchestration.
Create internal tools, frameworks, and CLI‑first utilities that improve team efficiency, accelerate experimentation, and support greenfield AI initiatives across TxDOT.
Collaborate with cross‑functional teams to translate ambiguous requirements into working AI solutions, providing technical leadership, identifying risks, ensuring compliance, and guiding the adoption of standardized AI governance practices.
#J-18808-Ljbffr