Dynamic Software Technical Lead with a strong record of delivering scalable, high-performance software solutions for machine learning and autonomous vehicle systems. Skilled in driving innovation, optimizing system performance, and successfully leading cross-functional teams to achieve impactful results.

* Responsible for the technical ownership of a mission-critical RESTful API that serves as the ground truth for data used by over 20 interconnected microservices and ML tools. This API underpins numerous ML training workflows, ensuring reliable data flow, consistency, and performance across the organization’s distributed systems (.NET Core, EntityFramework, C#, AWS Aurora, RDS, AWS Elastic Beanstalk, EC2, S3, SNS, SQS, Docker).

* Spearheaded the design and implementation of multiple microservices to automate previously manual workflows, achieving a 15x increase in processing speed and a 10x boost in labeling throughput. This transformation significantly streamlined operations, reduced human intervention, and enhanced overall system efficiency (CDK, AWS Lambda, DynamoDB, EKS, EventBridge, SNS, SQS, Step Functions).

* Proactively analyzed incoming independent projects to identify overlaps, taking ownership of architectural decisions and roadmap development to consolidate efforts, reduce redundancy, and streamline workflows. This initiative led to optimized architecture, decreased project complexity and shortened delivery timeline from 6 months to 3 months.

* Skilled in steering development teams through complex projects by prioritizing best practices and fostering a culture of technical excellence.

* Partnered with product managers and stakeholders to transform business requirements into effective technical solutions, ensuring clear communication and alignment across teams.

* Guided and mentored junior developers in technical skills and career growth, leading to two team members achieving well-deserved promotions.

Lead the design, development, and implementation of scalable and robust machine learning platform components that enable ML scientists to facilitate the data annotation processes.

Collaborate with cross-functional teams including data science, product management and IT/Security to architect and implement end-to-end solutions for various ML initiatives.

Design and implement automation tools, APIs and frameworks using AWS to streamline the machine learning workflows, data annotation pipelines and dataset delivery.

Owned the design and development of APIs to expose data coming from autonomous vehicles and to facilitate the data annotation processes.