Key Responsibilities:
Machine Learning Model Development:
Design, train, and optimize machine learning models for various applications (e.g., classification, regression, recommendation systems
Implement algorithms using popular frameworks (e.g., TensorFlow, PyTorch, Scikit-learn
Work with large datasets, data preprocessing, and feature engineering to enhance model performance.
Evaluate models, tune hyperparameters, and ensure models are production-ready.
UI/UX Design & De
|
 |
Job Description:
“Splunk ML toolkit” is must .• 6 to 8 years of Hands-on experience with Splunk, particularly the Machine Learning Toolkit (MLTK)• Utilize Splunk's advanced features, including data ingestion, processing, and visualization• Design, develop, and deploy machine learning models using Splunk's MLTK.• Create custom models and algorithms to identify patterns, predict trends, and automate anomaly detection.• Implement and optimize predictive analytics solutions to enhance operational
|
|
Introduction:We are looking for a skilled and motivated Machine Learning Engineer to join our dynamic team. The ideal candidate will have strong experience in Python programming, a deep understanding of machine learning algorithms, and a passion for solving complex problems using data-driven approaches. In this role, you will be responsible for developing and deploying machine learning models, analyzing data, and creating data-driven solutions.
Key Responsibilities:
Design and Build ML Mod
|
|
Key Responsibilities:Design, develop, and deploy machine learning models using TensorFlow and other machine learning frameworks.Collaborate with data scientists, software engineers, and other stakeholders to understand business requirements and translate them into technical solutions.Optimize machine learning models for speed, accuracy, and efficiency.Research and implement the latest advancements in deep learning, natural language processing (NLP), computer vision, and reinforcement learning.Cr
|
|
Required Skills and Qualifications:
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
Proven experience in building and deploying machine learning models (supervised, unsupervised, reinforcement learning, etc.
Strong programming skills in Python, R, or similar languages used in machine learning.
Solid understanding of data structures, algorithms, and software engineering principles.
Hands-on experience with machine learning libraries and
|
|
Key Responsibilities
Develop ML Models: Design, build, and deploy machine learning models for cybersecurity threat detection, anomaly detection, intrusion detection systems (IDS), malware analysis, and data classification.
Data Analysis: Work with large sets of security-related data, including logs, network traffic, and endpoint information, to identify patterns and potential threats.
Threat Intelligence: Integrate machine learning algorithms with threat intelligence sources to
|
|
Key Responsibilities:Develop and Integrate ML Models: Design, train, and integrate machine learning models into iOS applications. Utilize frameworks such as Core ML, Create ML, or TensorFlow Lite to deploy models on iOS devices.Optimize for Performance: Ensure that models are optimized for speed, memory usage, and battery efficiency on iOS devices, without sacrificing accuracy or functionality.Collaboration with Cross-functional Teams: Work closely with iOS developers, data scientists, and produ
|
|
Key Responsibilities:Machine Learning Development:
Design, develop, and deploy machine learning models for tasks such as anomaly detection, predictive analytics, and natural language processing (NLPWork with large datasets to train, test, and validate models.Continuously optimize models to improve performance, scalability, and accuracy.ServiceNow Integration:
Integrate machine learning models into ServiceNow to automate processes like incident management, service request handling, and predic
|
|
Key Responsibilities:
Develop, optimize, and deploy machine learning algorithms and models using C++.Work closely with data scientists and other engineers to integrate machine learning models into production environments.Design and implement scalable, high-performance solutions for processing large datasets in with cross-functional teams to understand business needs and translate them into effective machine learning solutions.Write clean, efficient, and well-documented code while maintaining
|
|
Job Responsibilities:Design, build, and maintain machine learning models and data pipelines to drive business insights and decisions.Collaborate with data scientists, analysts, and product teams to gather requirements and develop innovative solutions.Develop, test, and deploy production-ready machine learning models in Ruby-based environments.Integrate machine learning models into web applications and systems using Ruby on Rails or similar Ruby frameworks.Optimize models and algorithms to improv
|
|