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1 Day ago
Interesting Job Opportunity: Ksolves - Artificial Intelligence/Machine Learning Engineer - Data Modeling
Hyderabad, Telangana, India
Information Technology
Full-Time
Ksolves India Limited
Overview
Responsibilities
- Execute POCs : Develop proof of concepts (POCs) and basic model implementation with minimal guidance.
- Develop ML Models : Train and optimise basic machine learning models, perform data preprocessing, and carry out exploratory data analysis.
- Work with Cloud APIs : Use ML/AI APIs from GCP (preferred), AWS, or Azure for simple model development and integration tasks.
- Handle Datasets : Work with small to medium-sized datasets for analysis and model training, ensuring data accuracy and usability.
- Collaborate with Teams : Work closely with senior engineers and cross-functional teams to integrate ML models into applications.
- Learn Advanced Techniques : Be proactive in learning and applying advanced concepts in machine intelligence, including areas like anomaly detection, fraud detection, or AI IoT.
- Education : Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.
- Experience : 1+ year of experience in machine learning or data science, with a basic understanding of industry workflows.
- Programming Skills : Proficiency in Python with working knowledge of data manipulation and visualisation libraries like Scikit-learn and Pandas.
- Machine Learning Knowledge : Basic understanding of machine learning, statistical modelling, and AI domains such as NLP or computer vision, with the ability to build and deploy simple models.
- Cloud Platforms : Experience with cloud platforms (GCP preferred, AWS, Azure) and the ability to use their basic ML/AI services.
- ML DevOps and Virtualisation : Basic knowledge of versioning, model serving, and containerisation tools like Docker.
- Problem-Solving : Strong analytical and problem-solving skills, with a willingness to take on challenges in a dynamic environment.
- Communication Skills : Ability to clearly communicate technical findings to peers and stakeholders. (ref:hirist.tech)
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