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# Sr. Machine Learning Engineer

- **Company:** K-Tek Resourcing LLC
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Computer Vision, Microsoft Azure, Big Data, Cloud Computing, Data Cleansing, Information Engineering, Distributed Systems, Apache Hadoop, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Tensorflow, Standard Sql, SQL Databases, Jupyter Notebook, Google Cloud, Feature Engineering, Pytorch, Apache Spark, Deep Learning, Model Validation, Git, Pandas, Scikit Learn, Kubernetes, Machine Learning Operations, Data Pipelines, Docker, Databricks
- **Published:** June 24, 2026
- **Apply:** https://www.dice.com/job-detail/6e6e0d18-9d4f-4cd3-bd15-8c82bfe1ee87

## About the Role

* Python, SQL
 * PyTorch, TensorFlow, Scikit-learn
 * Pandas, NumPy, Jupyter Notebook
 * Statistics and Probability
 * Feature Engineering and Model Evaluation
 * Deep Learning, NLP, Computer Vision (depending on the role)
 * MLOps, Docker, Kubernetes, MLflow
 * Cloud Platforms (AWS, Azure, Google Cloud Platform)
 * Spark, Databricks, Hadoop
 * Git, CI/CD pipelines
 * Data Modeling and Data Engineering concepts

## Description

* Design, develop, train, and deploy machine learning models to solve business problems.
 * Build and maintain scalable data pipelines for collecting, processing, and transforming large datasets.
 * Perform feature engineering, data preprocessing, and exploratory data analysis.
 * Evaluate and optimize model performance using appropriate metrics and validation techniques.
 * Implement and fine-tune algorithms using frameworks such as PyTorch, TensorFlow, and Scikit-learn.
 * Develop and maintain production-ready ML solutions and APIs.
 * Collaborate with data scientists, data engineers, software developers, and business stakeholders to translate requirements into AI-driven solutions.
 * Monitor deployed models, identify performance degradation, and retrain models as needed.
 * Apply statistical analysis and mathematical modeling techniques to derive insights and improve predictions.
 * Automate model training, testing, and deployment processes using MLOps practices and CI/CD pipelines.
 * Work with cloud platforms such as AWS, Azure, or Google Cloud Platform to build and manage ML infrastructure.
 * Utilize distributed computing and big data technologies such as Spark, Databricks, and Hadoop for large-scale model training.
 * Develop and optimize SQL queries for extracting and analyzing structured data.
 * Create clear technical documentation, model reports, and presentations for stakeholders.
 * Ensure model governance, data quality, security, and compliance with organizational standards.
 * Research and evaluate new machine learning techniques, tools, and emerging AI technologies.


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