Reply requested for below job form TXDoT
Artificial Intelligence/Machine Learning Engineer
Austin, TX ONSITE
Data Preparation & Integration:
Collect, clean, and transform data from multiple sources.
Assist with ETL pipelines and improve workflow efficiency.
Analytics & Modeling:
Support predictive models and statistical analysis.
Perform exploratory data analysis to generate business insights.
Collaboration:
Work with senior team members on projects.
Join code reviews and contribute to documentation.
1-2 years of academic or internship work in data projects.
Understanding of data engineering and analytics methods.
Hands-on with machine learning frameworks and deep learning concepts (CNNs, RNNs, transformers).
Experience with NLP libraries (spaCy, Hugging Face) and MLOps tools (Docker, Kubernetes, MLflow).
Skilled in feature engineering for large datasets, using cloud AI services, and evaluating predictive models.
Interest in generative AI and LLMs.
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, Google Cloud Platform, or OCI for deploying and managing ML workloads. We leverage AI/ML tools across all major cloud providers (Azure AI, AWS SageMaker/Bedrock, Google Cloud Platform 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
vkm
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