Senior AI Engineer

iCliniq is a pioneering global healthcare technology company committed to democratizing

access to world class medical care. Our mission is to leverage cutting edge technology to

ensure everyone, regardless of location, has access to top tier healthcare services. By

integrating innovative AI solutions and telemedicine, we bridge the gap between patients and

healthcare professionals, providing seamless and efficient medical care worldwide.

We are currently seeking talented and passionate engineers to join our dynamic team with a

culture of collaboration and continuous learning. If you are driven by innovation and eager to

make a significant impact in the healthcare industry, we would love to hear from you. Please

send your resume to hr@icliniq.com . We look forward to connecting with you.

Role:

Experience:

Work location:

iCliniq’s AI Engineer core responsibilities:

1. Conceptualizing and Designing AI Solutions:

● Identifying AI Opportunities: You will work closely with business stakeholders to

understand their challenges and identify areas where AI can provide significant

advantages. This involves analyzing data, processes and workflows to pinpoint

opportunities for automation, optimization and prediction.

● Solution Architecting: Once an opportunity is identified, you will design the

technical architecture of the AI solution. This involves selecting appropriate

machine learning models, outlining data pipelines and defining the integration

points with existing systems.

Building and Training AI Models:

● Data Acquisition and Engineering: You will collaborate with data engineers to

acquire, clean and prepare datasets for training machine learning models. This

may involve data wrangling, feature engineering and ensuring data security and

privacy.

● Model Development and Training: You will develop and train models on the

prepared data. This involves choosing the right algorithms, hyperparameter

tuning and iteratively improving the model's performance through rigorous testing

and evaluation.

Deployment and Management:

● Model Integration and Deployment: You will ensure smooth integration, handle

infrastructure considerations and monitor the performance of deployed models.

● Performance Monitoring and Optimization: You will also continuously monitor the

performance of AI models, identify potential biases or drifts and perform

necessary optimizations to maintain accuracy and efficiency over time.

Collaboration and Communication:

● Cross Functional Collaboration: You will collaborate with data scientists, software

engineers and product managers throughout the development lifecycle. Effective

communication and knowledge sharing are essential for a successful outcome.

● Stakeholder Management: You are expected to present findings, explain

technical concepts in clear language and manage expectations throughout the

tenure.

● Documentation and Reporting: In addition to presenting findings and managing

expectations, you are required to document data sources, model architectures,

training procedures, evaluation metrics and deployment processes.

Regulatory Compliance and Ethical Considerations:

● Regulatory Compliance: Solutions you define may need to comply with various

regulatory standards and guidelines. Responsibilities related to ensuring

regulatory compliance, such as data protection laws (e.g., GDPR), industry

specific regulations (e.g., healthcare regulations) and ethical guidelines (e.g.,

IEEE Ethically Aligned Design..

● Ethical Considerations: You are also required to keep in mind issues such as bias

mitigation, fairness, transparency and privacy protection in AI systems that you

build.

. Continuous Learning:

● Staying Current with AI Trends: You are responsible for staying up to date with

the latest advancements in algorithms, frameworks and ethical considerations

surrounding AI.