Position: ML Architect
Experience: 12-15 years
Location: Bangalore / Trivandrum
Our client is looking for an innovator with 12-15 years of experience proven technical
leadership in design, implementation and support of large-scale systems and solutions
that are highly available, reliable, flexible, and scalable.
Roles and Responsibilities
- Lead multiple AI/ML initiatives.
- Given a customer problem statement, estimate solution feasibility and potential approaches based on available data
- Quickly prototype solutions and build models to test feasibility of solution approach
- Build ML models, train and test and optimize model performance
- Collaborate with product development teams to industrialize machine learning models and solutions.
- Engage with external ecosystem (academia, technology leaders, open source etc.) to develop the skills and technology portfolio.
- Manage communication, planning, collaboration, and feedback loops with business stakeholders.
- Research and implement novel ML and DL approaches.
- Lead the data science teams and ensure alignment with product and business strategy.
- The problem statement covers aspects Computer Vision, Document Extraction,
- Building Knowledge Graphs & AML.
- Strong background in Statistics and Machine Learning.
- Ability to perform independent research across various domains of analytics.
- Experience with developing advanced data science models and computational algorithm
- Hands on experience with advanced Data/Text Mining/NLP/Computer Visio
Strong Programming skills in Python
- Hands-on experience in designing and building AI models using Deep Neural
- Network using any of frameworks such as Keras/TensorFlow/ Pytorch.
- Knowledge of building explainable models (XAI)
- Knowledge of ML Ops concepts
- Experience with the development of REST APIs and HTTP method
- Experience with relational (MySQL) and non-relational / document databases (MongoDB/Couch DB.)
- Experience of working with Cloud Providers like Microsoft Azure/AWS
- Domain experience in Banking/Finance
- Conceptual understanding graph database.
- Fundamental concepts in the field of information retrieval, graph mining,
- knowledge representation – ontologies etc.
- Demonstrated participation in contests like Kaggle is a plus