• First Forward Deployed AI Engineer hired to design, build, and operationalize AI agents for large enterprise clients, starting with Crest LLC and its multi-unit business operations.

• Architect end-to-end AI workflows by deeply understanding client processes, mapping business requirements, and converting complex workflows into production-ready AI agent systems.

• Identify inefficiencies across legacy operations (procurement, compliance, pre-qualification, project controls, etc.) and design agentic automations that replace multi-week manual tasks with workflows that run in minutes.

• Build, refine, and deploy robust AI agents capable of document parsing, extraction, reasoning, workflow orchestration, SME escalation, and structured output generation across diverse enterprise use cases.

• Collaborate directly with client stakeholders (executives, PMs, engineers, SMEs) to validate requirements, optimize workflows, and deliver solutions that immediately improve operational efficiency.

• Own full deployment lifecycle — from scoping and prompt-engineering to evaluation, testing, iteration, and cross-team integration with engineering and product.

• Drive measurable business outcomes, including dramatic reductions in process turnaround times, improved accuracy of critical workflows, and enhanced data consistency across business units.

• Serve as the bridge between product, engineering, and enterprise clients, ensuring that agent capabilities align with real-world constraints and deliver production-grade reliability.

I am currently part of the Software Engineering Residency Fellowship at Headstarter, a selective program designed to accelerate technical growth through intensive, real-world projects. In this fellowship, I collaborate closely with startup founders and like-minded engineers on two-week sprint cycles, where each project is built to be production-ready and demonstrates end-to-end ownership.

The program emphasizes practical, industry-relevant engineering skills, from designing scalable architectures and implementing robust backends to building intuitive frontends and deploying applications in production environments. Beyond coding, I’ve also had the opportunity to work in cross-functional teams, practice agile workflows, and present deliverables under tight timelines, which has sharpened both my technical and collaboration skills.

This residency has given me hands-on exposure to modern stacks, AI/LLM integrations, workflow automation, and cloud deployments, while also fostering a mindset of rapid iteration, problem-solving, and delivering real value to users. It has been instrumental in preparing me to contribute effectively in fast-paced startup environments.

• Designed and built user-friendly interfaces for data analysis tools in wearable technology research, ensuring intuitive access to complex functionalities.

• Assisted in developing and testing algorithms for the W4H Toolkit project, collaborating with research teams to improve algorithm performance.

• Engaged in collaborative research with a multidisciplinary team, providing technical insights and documenting development processes.

• Designed and built user-friendly interfaces for data analysis tools in wearable technology research, ensuring intuitive access to complex functionalities. • Assisted in developing and testing algorithms for the W4H Toolkit project, collaborating with research teams to improve algorithm performance.

• Engaged in collaborative research with a multidisciplinary team, providing technical insights and documenting development processes.

* Integrate tools for platform development, like -

* Dalle, S3 buckets, Mem0, etc

* Built data analytics dashboard to analyze customer usage patterns and reflect insights on Sim Studio
Integrate tools for platform development, like - Dalle, S3 buckets, Mem0, etc Built data analytics dashboard to analyze customer usage patterns and reflect insights on Sim Studio

• Developed and deployed Python-based automation scripts and scalable ETL/data pipelines on Microsoft Azure, reducing CPU utilization by up to 79% and lowering operational costs by 60–70%.

• Implemented AI-driven analytics and deep learning models with Azure AI to forecast VM usage, improving cloud resource allocation and supporting sustainability initiatives by reducing energy consumption.

• Designed and integrated job scheduling workflows in Python to minimize unnecessary machine runtimes, enhancing system efficiency and reducing idle compute hours.

• Built monitoring and alerting solutions with Python, Azure Monitor, and logging frameworks, improving system reliability and reducing downtime by 30%.

• Optimized SQL queries and Python data processing scripts for large-scale datasets, reducing execution times and accelerating analytics workflows.

• Collaborated with cross-functional teams in an Agile environment to deliver automation solutions, ensuring alignment with business goals and scalability requirements.