Ai/ml Architect

5 months ago

Bengaluru, India ExamRoom.AI Full time

**Reports To**:CTO**

**Position Required Skills, Knowledge, and Abilities**:

- 5 years in designing high-level architectures and solutions for large-scale ML systems and/or building solutions for a product on AI/ML features/capabilities.
- Strong background in industry use cases built on deep learning and machine learning (unsupervised and supervised techniques) is a must.
- Deep learning frameworks such as TensorFlow, Keras, PyTorch
- Time series analysis, anomaly detection, forecasting, predictive modeling, graph-based neural networks, and Bayesian statistics are good to have.
- Deep learning architectures like RNN, CNN, LSTM, and Transformers
- Strong command of Python or equivalent language for ML
- Graph databases, analytics, and graph neural networks
- Data structures and ability to develop efficient solutions.
- Excellent verbal and written communication skills, especially the ability to share technical results and recommendations with both, technical and non-technical audiences.
- Ability to perform high-level work both independently and collaboratively as a project member or leader on multiple projects.

**Position Education and Experience (required and preferred)**:

- B.E./ B.Tech. degree in Computer Science or equivalent from a reputed institute
- An advanced degree in predictive analytics, machine learning, or artificial intelligence; or a degree in programming and significant experience with text analytics / NLP

**Position Responsibilities**:

- Showcase business acumen to translate requirements into ML problems.
- Lead ML teams and build, designed, and drive solutions to deliver.
- Guide teams in evaluating the quality of ML models and, review and define the right performance metrics for models in accordance with the requirements of the core platform.
- Be responsible for building new and innovative solutions leveraging data science and artificial intelligence skills/technologies to solve non-trivial problems.
- Lead teams of data scientists and ML engineers, reviewing the teams work and troubleshooting models implemented.

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