AI/ML Engineering Lead

3 weeks ago

chennai, India Saaki Argus & Averil Consulting Full time

Responsibilities:

1. Lead the design and development of AI/ML-based features and capabilities of the company's platform for remote patient monitoring and chronic care management.

2. Act as a technical expert and provide guidance to the development team on the best practices and technologies to use for AI/ML projects.

3. Collaborate with the product and data science teams to understand the product requirements and ensure that the development team can deliver the product on time and within budget.

4. Lead a team of AI/ML engineers and be responsible for the technical direction of the product.

5. Mentor, train, and provide guidance to the team members to ensure that they have the skills and knowledge to deliver the product.

6. Identify and mitigate technical risks and keep the Product Owner and other stakeholders informed of any issues or challenges that may impact the product development.

7. Ensure that the product is of high quality and that it meets the requirements of the customers.

8. Continuously evaluate and improve the product and ensure that the development team is using the latest technologies and best practices.

9. Ensure that the product is scalable, maintainable, and secure.

General Qualifications:

1. Strong understanding of AI/ML concepts and algorithms, such as reinforcement learning, deep learning, natural language processing, computer vision, and statistical modeling.

2. Experience with implementing AI/ML models and algorithms in a production environment.

3. Knowledge of popular AI/ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, etc.

4. Experience in working with large datasets and using data pre-processing techniques to prepare data for training models.

5. Experience in deploying AI/ML models such as AWS SageMaker, Google Cloud ML Engine, or Azure Machine Learning.

6. Familiarity with model building, for example (illustratively)

a. Reinforcement learning models for patient behavior nudging

b. Predictive models for patient risk stratification

c. Natural Language Processing models for extracting information from unstructured data such as electronic medical records

d. Computer Vision models for analyzing medical images

e. Time-series forecasting models for predicting patient outcomes and resource utilization

f. Anomaly detection models for identifying unusual patterns in patient data

g. Generative models for synthesizing realistic patient data for simulation and testing

7. Understanding of evaluation metrics and techniques for model optimization.

8. Strong understanding of the ethical and legal considerations surrounding AI/ML, such as bias and explainability.

9. Proven experience in leading AI/ML projects and teams.

10. Strong coding skills in Python and experience with popular AI/ML frameworks such as TensorFlow and PyTorch.

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