Top Benefits
About the role
Hybrid: 3 Days onsite / 2 days remote
Must Haves:
- Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts, algorithms, and techniques.
- Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models, especially BERT and other transformer-based models, for tasks like text classification, sentiment analysis, and language understanding.
- Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing, training, and fine-tuning BERT models using these frameworks is crucial.
- Data Preprocessing Skills: Ability to perform text preprocessing, tokenization, and understanding of word embeddings.
- Programming Skills: Strong programming skills in Python, including experience with libraries like NumPy, Pandas, and Scikit-learn.
- Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
- Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.
Experience and Skill Set Requirements
-
(15%) Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts, algorithms, and techniques.
-
Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models, especially BERT and other transformer-based models, for tasks like text classification, sentiment analysis, and language understanding.
-
(20%) Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing, training, and fine-tuning BERT models using these frameworks is crucial.
-
(30%)Data Preprocessing Skills: Ability to perform text preprocessing, tokenization, and understanding of word embeddings.
-
Programming Skills: Strong programming skills in Python, including experience with libraries like NumPy, Pandas, and Scikit-learn.
-
(20%) Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
-
(15%) Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.
About Tangentia
Tangentia is the leader in Autonomous Enterprise solutions with solutions around 3 focus areas.
Intelligent Automation- Artificial Intelligence(AI), Robotic Process Automation(RPA) and Blockchain B2B Connectivity - EDI, Ecommerce, OMS and Supply Chain Digital Innovation - Agile App Development, Strategic Staffing,Mobile, Salesforce & IBM consulting Tangentia customers include Royal Bank of Canada, Liquor Control Board of Ontario(LCBO),Suncor, Reliance Retail, Saint Gobain, Whirlpool, Sasktel, Vanguard and others. Tangentia has 12 Fortune 500 customers and 1000+ mid sized companies globally. Our Cloud SaaS solutions like Tangentia Gateway process more than US $10 Billion in transaction value annually.
Tangentia is a platinum partner of IBM with strong global capabilities around Watson AI and Sterling. In addition, other major partnerships include Automation Anywhere, Microsoft, Salesforce, BluePrism, UI Path, Google AI, and AWS. Headquartered in Toronto, Canada, Tangentia also has offices in the USA, Mexico, and India. Through our global offices as well as associates & ecosystem partners, we have a presence in 5 continents and 30+ countries, offering a "think global, act local" solutions approach.
Learn more at www.tangentia.com
Top Benefits
About the role
Hybrid: 3 Days onsite / 2 days remote
Must Haves:
- Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts, algorithms, and techniques.
- Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models, especially BERT and other transformer-based models, for tasks like text classification, sentiment analysis, and language understanding.
- Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing, training, and fine-tuning BERT models using these frameworks is crucial.
- Data Preprocessing Skills: Ability to perform text preprocessing, tokenization, and understanding of word embeddings.
- Programming Skills: Strong programming skills in Python, including experience with libraries like NumPy, Pandas, and Scikit-learn.
- Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
- Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.
Experience and Skill Set Requirements
-
(15%) Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts, algorithms, and techniques.
-
Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models, especially BERT and other transformer-based models, for tasks like text classification, sentiment analysis, and language understanding.
-
(20%) Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing, training, and fine-tuning BERT models using these frameworks is crucial.
-
(30%)Data Preprocessing Skills: Ability to perform text preprocessing, tokenization, and understanding of word embeddings.
-
Programming Skills: Strong programming skills in Python, including experience with libraries like NumPy, Pandas, and Scikit-learn.
-
(20%) Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
-
(15%) Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.
About Tangentia
Tangentia is the leader in Autonomous Enterprise solutions with solutions around 3 focus areas.
Intelligent Automation- Artificial Intelligence(AI), Robotic Process Automation(RPA) and Blockchain B2B Connectivity - EDI, Ecommerce, OMS and Supply Chain Digital Innovation - Agile App Development, Strategic Staffing,Mobile, Salesforce & IBM consulting Tangentia customers include Royal Bank of Canada, Liquor Control Board of Ontario(LCBO),Suncor, Reliance Retail, Saint Gobain, Whirlpool, Sasktel, Vanguard and others. Tangentia has 12 Fortune 500 customers and 1000+ mid sized companies globally. Our Cloud SaaS solutions like Tangentia Gateway process more than US $10 Billion in transaction value annually.
Tangentia is a platinum partner of IBM with strong global capabilities around Watson AI and Sterling. In addition, other major partnerships include Automation Anywhere, Microsoft, Salesforce, BluePrism, UI Path, Google AI, and AWS. Headquartered in Toronto, Canada, Tangentia also has offices in the USA, Mexico, and India. Through our global offices as well as associates & ecosystem partners, we have a presence in 5 continents and 30+ countries, offering a "think global, act local" solutions approach.
Learn more at www.tangentia.com