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# Senior Technical Lead - Computer Vision

- **Company:** HCLTech
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Computer Vision, Software Quality, Python (Programming Language), Machine Learning, NumPy, Performance Tuning, Software Tools, Tensorflow, Feature Engineering, Pytorch, Apache Spark, Deep Learning, Model Validation, Pandas, Scikit Learn, Optimization Algorithms, Xgboost, Apache Kafka, Machine Learning Operations, Feature Extraction, Data Pipelines, Unsupervised Learning
- **Published:** July 23, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=a9cc73972332f227

## About the Role

1. Advanced Proficiency In Machine Learning (Vision), Deep Learning, And Computer Vision Algorithms.
 2. - Solid Experience With Python, Tensorflow, Pytorch, And Scikit-Learn For Model Development And Evaluation.
 3. - In-Depth Knowledge Of Data Engineering Tools Including Apache Spark, Kafka, And Airflow For Scalable Ml Workflows.
 4. - Strong Understanding Of Model Evaluation Metrics Such As Roc/Auc, Precision/Recall, F1-Score, And Confusion Matrix.
 5. - Advanced Skills In Feature Engineering, Supervised/Unsupervised Learning, And Optimization Techniques.
 6. - Good Familiarity With Numpy, Pandas, Xgboost, Lightgbm, And Related Ml Libraries.

## Description

This role is responsible for driving the development and deployment of advanced computer vision solutions within complex projects. The individual applies advanced proficiency in machine learning, deep learning, and vision algorithms to deliver robust models, optimize workflows, and provide strategic technical guidance to team members. They ensure the successful delivery of high-impact solutions aligned with organizational goals., 1. Develop and optimize computer vision models using Python, TensorFlow, and PyTorch, applying advanced proficiency in supervised and unsupervised learning techniques to solve complex image and video analysis tasks.

 2. Implement scalable data pipelines with Apache Spark and Kafka, ensuring efficient ingestion, processing, and transformation of large-scale visual datasets for model training and inference.
 3. Evaluate machine learning models using cross-validation, ROC/AUC, F1-score, and confusion matrix, providing advanced insights to improve model accuracy and reliability.
 4. Apply deep learning frameworks and libraries such as scikit-learn, XGBoost, and LightGBM to enhance feature extraction and classification performance in vision projects.
 5. Guide team members in adopting best practices for code quality, model deployment, and performance optimization, leveraging tools like Apache Airflow for workflow automation.
 6. Collaborate with internal stakeholders to define technical requirements and deliverables, ensuring alignment of vision solutions with project objectives and compliance standards.


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