Digital Health Integration Specialist (Applied AI)
Support and drive the Applied AI for Digital Health Integration track.
Overview
Support and drive the Applied AI for Digital Health Integration track.
You have:
- Bachelor's or master's degree in computer science, data science, artificial intelligence or machine learning, engineering, or a closely related field.
- 5 or more years of experience in AI, machine learning, data science, or software engineering, including hands-on delivery of AI or data-driven solutions into production.
- Demonstrated experience with applied AI, such as large language models, agentic AI workflows, or end-to-end machine-learning pipelines.
- Familiarity with APIs and microservices, sandbox or developer-integration environments.
- Proven performance in a fast-paced, results-driven environment.
- Good understanding of the DPDP Act 2023 and health data governance, including consent, privacy, data minimisation, and anonymisation.
- Strong, hands-on technical skills in Python and modern AI, machine-learning, and LLM or agentic frameworks, with a solid grasp of data engineering, APIs, and cloud environments (e.g., AWS, Google Cloud, or Azure).
- Hands-on experience with data pipeline/ETL (e.g., Airflow, dbt, or equivalent) and in version control and collaborative engineering practices (e.g., Git, code review, automated testing).
- Ability to translate policy and functional requirements into technical designs and working systems, and to reason about trade-offs in speed, accuracy, resilience, and cost.
- Fluency in English. Fluency in Hindi or an additional Indian language is an advantage.
Potential interview questions
| Can you describe a time when you successfully implemented an AI solution in a fast-paced environment? | This question assesses your practical experience with AI under high-pressure circumstances. | Focus on specific challenges and your approach to integrating AI solutions. |
| What techniques would you use to ensure compliance with the DPDP Act 2023 in your projects? | The interviewer is looking for your understanding of health data governance. | Pro members can see the explanation. |
| How would you approach the design of an agentic AI workflow? | Pro members can see the explanation. | Pro members can see the explanation. |
| Can you discuss your experience with large language models and any challenges you've faced? | Pro members can see the explanation. | Pro members can see the explanation. |
| Describe how you would handle ambiguity in project requirements. | Pro members can see the explanation. | Pro members can see the explanation. |
| How do you ensure the reliability and scalability of data pipelines? | Pro members can see the explanation. | Pro members can see the explanation. |
| What are your strategies for integrating feedback into your technical designs? | Pro members can see the explanation. | Pro members can see the explanation. |
| How do you stay updated with the latest trends in AI and health data governance? | Pro members can see the explanation. | Pro members can see the explanation. |
Added 5 days ago
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Updated 3 days ago
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Source:
clintonhealthaccess.org
