• Built and optimized voice-first generative AI agents for healthcare, addressing use cases such as post-discharge follow-ups, screening outreach, scheduling, billing explanations, and chronic-care check-ins.

• Contributed to development of Polaris 3.0, a 4.2T-parameter LLM constellation of 22 models, improving clinical accuracy to 99.38%.

• Integrated agent workflows with major EMR systems (Epic, Cerner, Salesforce, Athena), enabling seamless provider and payor adoption.

• Supported launch of the AI Agent App Store, enabling licensed clinicians to design and monetize safe, specialty-specific healthcare agents.

• Enhanced patient experience with multilingual agents, including bilingual deployments such as WellSpan’s Ana for Spanish-speaking CRC screening outreach.

• Collaborated on safety pipelines using automated evals and 6,000+ clinician reviewers across 307K+ simulated calls, ensuring zero severe-harm errors.

• Delivered measurable patient engagement outcomes: 89.3% connect rate, 96.5% call completion, and satisfaction score improvement from 8.72 → 8.95/10.

• Integrated Airkit.ai’s no-code AI agent platform into Salesforce Service Cloud, enabling end-to-end customer service automation across chat, SMS, voice, and email.

• Contributed to foundational work that evolved into Salesforce Agentforce, the company’s cross-cloud autonomous AI agent platform.

• Delivered technical enhancements ensuring agent workflows operated seamlessly within the Einstein 1 / Customer 360 stack.

• Built code-free AI service agents handling commerce workflows such as order status, returns/exchanges, product issues, and support across chat, SMS, voice, and email.

• Developed API-driven customer experiences using REST connectors and integrations with platforms including Zendesk, Twilio, Stripe, and SAP.

• Implemented low-code orchestration pipelines enabling enterprises to launch omnichannel agents without custom infrastructure.

• Contributed to performance optimization of agent runtime, ensuring high-volume request handling and reliable automation across Fortune 500 deployments.

• Authored internal engineering documentation and sample applications, including

• Applied ML to large in-vitro screening datasets, uncovering novel drug–mutation interactions, including azithromycin’s sensitivity to KLK6 mutation status.

• Implemented machine learning pipelines to identify cancer cell vulnerabilities associated with TERT expression, surfacing dependencies in HDAC1 and Complex I genes.

• Built scalable data processing workflows to integrate diverse clinicogenomic and biomedical datasets for precision medicine research.

• Contributed to early biomarker discovery models supporting patient stratification for oncology trials and targeted therapy development.

• Co-authored and validated ML approaches documented in Oncogene (2021) publications linking drug responses with cancer cell biomarkers.