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# Senior Technical Lead - Traditional ML

- **Company:** HCL America Inc.
- **Location:** San Antonio, TX, United States
- **Experience:** Expert
- **Salary:** $148,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Big Data, Data Integration, Distributed Computing Environment, Apache Hadoop, Python (Programming Language), Machine Learning, Natural Language Processing, NLTK (NLP Analysis), NumPy, Performance Tuning, Tensorflow, Data Streaming, Workflow Management Systems, Pytorch, Apache Spark, Deep Learning, Pandas, Scikit Learn, Statistics Packages, Xgboost, Apache Kafka, Data Management, Software Coding, Spacy
- **Published:** September 9, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=74e25b8ba4fa3c7d

## About the Role

1. Advanced Proficiency In Machine Learning And Natural Language Processing, With Solid Experience In Time Series Analysis And Forecasting.
 2. Strong Skills In Python Programming, Including Numpy, Pandas, Scikitlearn, Tensorflow, Pytorch, Xgboost, And Lightgbm.
 3. Indepth Knowledge Of Distributed Data Processing With Apache Spark And Realtime Data Integration Using Apache Kafka.
 4. Solid Understanding Of Ml Model Evaluation Metrics And Techniques, Including Crossvalidation And Performance Optimization.
 5. Experience With Workflow Orchestration Tools Such As Apache Airflow And Big Data Platforms Like Hadoop.
 6. Advanced Proficiency In Nlp Libraries Including Nltk And Spacy., 2. Certifications Such As Tensorflow Developer Certificate
 3. - Aws Certified Machine Learning � Specialt

## Description

This role is accountable for developing, optimizing, and deploying advanced machine learning and natural language processing solutions focused on time series forecasting. The individual applies solid expertise in Python, distributed data processing, and ML model evaluation to deliver complex predictive models, ensuring technical excellence and successful project outcomes within the development team., 1. Develop and implement time series forecasting models using Python, scikit-learn, TensorFlow, and StatsModels, optimizing for accuracy and scalability.

 2. Integrate and process large-scale streaming data with Apache Kafka and Spark, enabling real-time model training and inference.
 3. Evaluate machine learning models using cross-validation, ROC/AUC, Precision/Recall, F1-score, and confusion matrix to ensure robust predictive performance.
 4. Apply advanced NLP techniques with NLTK and SpaCy to extract and preprocess relevant features for forecasting tasks.
 5. Optimize model deployment pipelines using Apache Airflow and Hadoop, ensuring efficient workflow orchestration and data management.
 6. Collaborate within the development team to troubleshoot, refine, and enhance ML solutions, ensuring adherence to best practices and coding standards.


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