• As the platform scaled to serve Fortune 100 enterprises, there was a need for a robust bulk operations capability touching audit, security, workflows, and archiving — owned the full technical design and end-to-end delivery of this initiative across multiple services and systems, coordinating cross-team alignment and resolving architectural ambiguities — shipped ahead of schedule, unblocking downstream product work and establishing a reusable delivery pattern for the team.

• As the enterprise demand for responsible AI grew, governance and compliance became a critical platform capability — contributed to designing and scaling the AI governance layer, enabling enterprises to maintain visibility, control, and compliance across their ML model lifecycle — helping the platform become a trusted system of record for AI operations at scale.

• As the platform grew in complexity, gaps in auditability and traceability created friction for enterprise compliance teams — led improvements to the audit and filtering infrastructure, including audit trail filtering, enriched data views, and filtered exports — delivering a more observable and compliant platform experience for regulated industries.

* Architected and implemented the Foghorn Cloud and FogHorn Edge AI platform (both cloud and embedded).

* Spearheaded cloud-agnostic product development, seamlessly integrating with GCP, Azure, and AWS.

* Demonstrated expertise in deploying scalable and high-availability cloud services using AWS and Azure, along with familiarity with other cloud platforms like GCP.

* Advanced proficiency in Java, Python, including experience in extracting data from APIs, building data pipelines, and utilizing Python for machine learning tasks.

* Agile methodology expertise for rapid iteration cycles in API and SDK-driven application development.

* Improved system integrations by developing SDKs in multiple languages, including Java, Rust, C#, and Python.

* Revamped the existing infrastructure to enable scalability, accommodating from 1,000 to 50,000 connected devices.

* Standardized communication protocols between systems to enhance reliability in connection and data transfer.

* Streamlined the DevOps pipeline by implementing multi-stage Dockerfiles and BuildKit, creating sanitized build environments.

* Proficient in Docker and Kubernetes for streamlined containerized application development.

* Skilled in NoSQL database platforms for efficient data management and retrieval.

* Conducted product feature training for onboarding and interviews.

* Removed significant developer/build inefficiencies by splitting monolithic repositories.

* Revamped the system to incorporate machine learning capabilities, spanning Flare Monitoring, Health Monitoring, Safety Monitoring, Hazard Detection, Energy Management System, and Building Management Systems.

* Designed and Implemented Edge-to-Cloud Data Pipelines: Developed robust data ingestion and processing pipelines, ensuring seamless data flow between edge devices and cloud environments.

* Optimized Performance of Edge AI Models: Reduced inference latency and resource consumption by implementing hardware-accelerated AI solutions tailored for edge devices.

* Pioneered Predictive Maintenance Solutions: Leveraged machine learning models to proactively identify and address potential system failures, minimizing downtime and improving reliability.

* Enhanced Security in Cloud-Edge Communication: Implemented advanced encryption protocols and secure authentication mechanisms to safeguard data transfer between edge and cloud.

* Automated Deployment Processes: Created CI/CD pipelines using Jenkins and GitLab CI, drastically reducing time-to-production for both cloud and edge platforms.

* Improved System Observability and Monitoring: Integrated logging, tracing, and monitoring tools such as Prometheus and Grafana to provide actionable insights into system performance and health.

* Led Cross-Functional Collaboration for AI Integration: Coordinated between data scientists, DevOps engineers, and software developers to deliver cohesive AI-driven features.

* Implemented Scalable Multi-Tenant Architectures: Designed cloud systems that supported multiple customers with isolated data and configurable processing pipelines.

* Developed Real-Time Event Processing Systems: Engineered low-latency systems capable of processing and reacting to high-frequency sensor data in real time.

* Authored Comprehensive Documentation for SDKs and APIs: Produced detailed developer guides, improving onboarding efficiency and reducing support queries for third-party integrators.

* Ensure the smooth operation and ongoing maintenance of software systems supporting the Ronald Tutor Campus Center's daily functions.

* Develop and manage event coordination tools, facilitating streamlined setup, scheduling, and logistics for events held at the Ronald Tutor Campus Center.

Foghorn Product:

* Integrated the systems to support secure access with Azure Active Directory for Identity Access and Management(IAM) using OAuth tokens.

* Developed Postgres Sink for product to publish data from sensors directly to Postgres database after flattening data.

* Enhanced connection to Kepware server using a Secure Socket Layer connection.

* Build Amazon S3 Sink and IOT Hub Sink to publish real-time data from channels to Sink using Apache Flume architecture.

* Developed docker containers to create an UAA client authentication access.