Manish Reddy Nandineni

At Revoint, my role has been to engineer core AI-driven systems from the ground up, taking them from initial proof-of-concept to production ready platforms. This work involves designing for scale, reliability, and direct business impact in a fast paced startup environment, with a focus on both high throughput data processing and intelligent workflow automation. I engineered a multi-agent AI system using CrewAI and the Gemini API with distinct research, strategy, content, and QA agents. This system cut marketing content cycles from 2 months to 1 week and successfully onboarded 4 businesses within the first month of launch. I also designed and built a high-throughput AWS pipeline to process over 300,000 documents (~5 TB) for M&A due diligence. The system used S3, ECS, DynamoDB, and OpenSearch, reducing cross-departmental review timelines from 3 months to 2 weeks. Additionally, I prototyped and benchmarked an AI system with Gemini and FAISS to automate ERP schema mapping across tens of thousands of tables. Early results demonstrated a potential 90% reduction in manual effort with a human-in-the-loop review process.

As an Associate Instructor for the Embedded Systems course (ENGR-E 314 / CSCI-P 442), I worked with Professor Bryce Himebaugh to help students translate complex engineering theory into practical, hands-on application. My mentorship focused on instilling sound engineering habits, including practical build quality and clear documentation. I guided a cohort of 40 students through intensive labs in C programming and STM32 microcontrollers. I provided direct technical support on debugging, calibration, and data capture, enabling them to successfully build and deliver working embedded light-detection prototypes.

Promoted to own backend performance for Capital Manager, I was tasked with solving the most critical stability challenges for our top enterprise clients. My role required a deep focus on diagnostics and performance tuning, as well as leading peer code reviews and mentoring junior engineers to elevate the team's quality standards. I led the initiative to stabilize the platform for Airbus’s largest-ever deployment. By resolving deep-seated memory and database bottlenecks, I enabled the system to support ~250 concurrent users, cut simulation times from days to 8-10 hours, and earned a Siemens Quarterly Performance Award for the impact. I also delivered a fast-tracked styling engine fix on a critical two-week timeline that secured ASML’s contract renewal.

As a core developer on the Capital Suite team, my work focused on shipping significant backend features and performance improvements that directly enhanced product value and stability for global clients. I designed and implemented a query-based styling extension that reduced customer workarounds by approximately 80%, improving maintainability and simplifying their workflows. I also contributed key multi-threading fixes to the migration framework, helping to reduce live upgrade windows from 3 days to under 24 hours.

I joined the Capital R&D team to support backend debugging, testing, and validation for enterprise electrical design tools. This role allowed me to build foundational skills in Java, multi-threading, and database optimization in a production environment. Under the guidance of senior engineers, I contributed code that was integrated into the final product.

During my internship, I contributed to a high-impact civic technology project, gaining hands-on experience in backend development, data automation, and quality assurance. My work supported the successful delivery of a critical public safety application. I developed automation scripts to streamline the import of data from multiple manual sources into MongoDB, improving data integrity and efficiency. I also created scripts to validate a NodeJS API by testing key network metrics, including ping, latency, and turnaround time. Working closely with developers and quality analysts, I helped resolve critical bugs during the pre-deployment phase, ensuring a stable launch for a women's safety application built in collaboration with the Hyderabad City Police Department.

Graduate coursework and projects focused on backend systems, cloud computing, and applied AI. Developed advanced understanding of distributed systems, machine learning, and product-oriented software design. Key coursework: Cloud Computing, Algorithms, Machine Learning, Software Engineering, Computer Networks, Product Management, Object-Oriented Software Development.