we are looking for a AI Solution Engineer.
The AI Solutions Engineer plays a critical, high-impact role in scaling our clinical AI solutions and ensuring the ability to deploy high quality products, monitor and support them in production.
This position requires strong software and data engineering skills to elevate our operational systems.
You will be responsible for independently driving complex E2E projects, demonstrating flexible thinking and making cost-effective decisions while balancing quality versus speed.
You will serve as a vital technical bridge, forming strong collaborations with both the R&D and Product organizations. Success in this dynamic and fast-driven environment demands a focused "getting things done" and "can-do" attitude to deliver value with speed and quality.
Responsibilities:
Automate and streamline AI operations – develop tools to automate AI deployment, data validation, and reporting for smooth production integration of AI solutions built by our research teams.
Act as the production technical expert – collaborate with Product and R&D teams during feature design to ensure production feasibility, scalability, and operational excellence.
Lead end-to-end tool development – gather requirements, design, implement, and deploy operational tools and pipelines in collaboration with cross-functional teams.
Drive engineering quality and play a key role in team member mentoring – uphold and develop engineering standards while fostering innovation.
Investigate and resolve complex data challenges – analyze and address production data issues, including data quality, availability, performance anomalies, and integration challenges.
Collaborate with customer-facing teams – understand real-world operational challenges, develop scalable solutions, and bridge the gap between customer needs and AI system behavior.
Ensure ongoing AI model reliability – design and implement mechanisms for monitoring data and performance degradation, and maintaining long-term model effectiveness.
Develop monitoring and analytics frameworks – create metrics and dashboards to track production health, data integrity, and AI performance across the deployment lifecycle.
The AI Solutions Engineer plays a critical, high-impact role in scaling our clinical AI solutions and ensuring the ability to deploy high quality products, monitor and support them in production.
This position requires strong software and data engineering skills to elevate our operational systems.
You will be responsible for independently driving complex E2E projects, demonstrating flexible thinking and making cost-effective decisions while balancing quality versus speed.
You will serve as a vital technical bridge, forming strong collaborations with both the R&D and Product organizations. Success in this dynamic and fast-driven environment demands a focused "getting things done" and "can-do" attitude to deliver value with speed and quality.
Responsibilities:
Automate and streamline AI operations – develop tools to automate AI deployment, data validation, and reporting for smooth production integration of AI solutions built by our research teams.
Act as the production technical expert – collaborate with Product and R&D teams during feature design to ensure production feasibility, scalability, and operational excellence.
Lead end-to-end tool development – gather requirements, design, implement, and deploy operational tools and pipelines in collaboration with cross-functional teams.
Drive engineering quality and play a key role in team member mentoring – uphold and develop engineering standards while fostering innovation.
Investigate and resolve complex data challenges – analyze and address production data issues, including data quality, availability, performance anomalies, and integration challenges.
Collaborate with customer-facing teams – understand real-world operational challenges, develop scalable solutions, and bridge the gap between customer needs and AI system behavior.
Ensure ongoing AI model reliability – design and implement mechanisms for monitoring data and performance degradation, and maintaining long-term model effectiveness.
Develop monitoring and analytics frameworks – create metrics and dashboards to track production health, data integrity, and AI performance across the deployment lifecycle.
Requirements:
Bachelor's degree in a relevant field (Computer Science, Data Science, Engineering). A Master's degree is an advantage.
At least 3 years of proven experience in software or data engineering in a professional setting.
Familiarity with statistical analysis, machine learning concepts, AI model evaluation and system engineering concepts.
Exceptional problem-solving skills and the ability to troubleshoot complex technical issues.
Strong collaboration and communication skills to work effectively with cross-functional teams.
Flexible and adaptive mindset – we need to deliver solutions with both quality and speed in mind, making decisions based on the dynamic tradeoffs between them along the way.
Experience in working in technically oriented operation environments is an advantage.
Tech stack:
Bachelor's degree in a relevant field (Computer Science, Data Science, Engineering). A Master's degree is an advantage.
At least 3 years of proven experience in software or data engineering in a professional setting.
Familiarity with statistical analysis, machine learning concepts, AI model evaluation and system engineering concepts.
Exceptional problem-solving skills and the ability to troubleshoot complex technical issues.
Strong collaboration and communication skills to work effectively with cross-functional teams.
Flexible and adaptive mindset – we need to deliver solutions with both quality and speed in mind, making decisions based on the dynamic tradeoffs between them along the way.
Experience in working in technically oriented operation environments is an advantage.
Tech stack:
This position is open to all candidates.
