Business analyst Milind Cherukuri builds scalable AI systems that deliver on ethics, performance

Salesforce business analyst and administrator at Caris Life Sciences, Milind Cherukuri plays a central role in shaping how artificial intelligence influences healthcare, enterprise operations, and scientific research.

The Institute of Electrical and Electronics Engineers named Milind Cherukuri a senior member in 2025, recognising his contributions to AI ethics and systems engineering.

With over 4,00,000 members worldwide, the recognition affirms Cherukuri’s peer-validated expertise, long-term impact, and leadership in engineering and technology. It also serves as a qualifying milestone toward the prestigious IEEE Fellow grade. The Clareus Scientific Society invited him to join its board the same year.

His prior engineering roles at Amazon and Infor allowed him to build systems that deliver measurable, ethical, and scalable outcomes.

Professional background and enterprise expertise

Milind's journey commenced in enterprise software development, crafting backend solutions and supporting large infrastructure projects. He developed ERP modules at Infor using Java, Spring, and Hibernate. At Amazon, he enhanced backend services, helping thousands of developers worldwide.

Now at Caris Life Sciences, Cherukuri leads improvements to Salesforce workflows across clinical, compliance, and operational teams. His automation allows organisations to substantially reduce errors and associated revenue losses.

These efforts improve data accuracy and regulatory compliance, which are vital for smooth patient onboarding in oncology.

Research contributions to AI accountability

He has authored five peer-reviewed papers addressing AI development challenges, interface validation, diagnostic imaging, and sentiment detection. His 2024 paper, “Advancing AI Safely,” presented at the EEET conference in Malaysia, offers practical strategies for auditing and safely deploying large language models.

“Large language models offer tremendous business potential. Organizations need structured methods to ensure these tools remain safe and transparent.” Cherukuri explains. “My research provides frameworks that help technical teams build these safeguards directly into their AI systems.”

He introduced a prompt rating system for clarity, performance, and cost; developed the WebChecker plugin to audit Bootstrap HTML designs; improved oncology diagnostics through segmentation algorithm analysis; and enhanced emotional recognition in mental health tools with a sentiment analysis framework.

Editorial leadership and mentorship

Milind Cherukuri joined the CS Science and Engineering Journal editorial board in 2025. He reviews submissions for journals such as AI for Our Planet, MDPI’s Journal of Imaging, and Jobari. He assesses methodological rigor and reproducibility in these roles.

“Editorial work involves more than reviewing articles,” Cherukuri notes. “It involves mentoring researchers, transparent methodology, and reproducible results ensure that published research genuinely advances our field.”

He mentors emerging researchers across North America, Asia, and Europe. He promotes clarity, transparency, and strong methodology in AI research publications.

International thought leadership and speaking engagements

He regularly presents at international conferences, focusing on practical applications of his research. He has spoken at EEET, ICDSCA, Fully3D, and DISCRETE, covering prompt engineering safety, AI diagnostics, and editorial practices. In March 2025, he addressed more than 300 graduate students at an IEEE Author Workshop Series on responsible research.

“Speaking to the next generation of AI developers is essential,” Cherukuri says. “Conversations about responsible technology use and rigorous research methods ensure that our innovations benefit society.” He later joined a global panel in May to discuss peer review integrity and reproducibility.

Developing transparent prompt engineering standards

Milind Cherukuri developed a standardised approach to prompt engineering by applying the same rigor in software development, including clear documentation, version control, and performance benchmarks. This framework enables organisations to evaluate and refine prompt effectiveness systematically.

Teams that adopted it reported up to a 35 percent reduction in inference costs, significantly outperforming what many in the industry believed prompt optimization could deliver.

According to recent reports, AI systems are already helping healthcare organizations reduce operational costs by as much as 30 percent, which reinforces the impact of Cherukuri’s structured and scalable solution.

“Prompt design must be transparent, testable, and repeatable,” Cherukuri explains. “This replaces guesswork with measurable standards.”

Cherukuri builds AI systems focused on practical functionality and lasting reliability. He improves clinical workflows, sets AI implementation standards, and mentors researchers. His work stresses accountability and measurable results.

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