Anannyaa Khedekar
I’m an AI-focused software engineer who builds reliable solutions at the intersection of Generative AI and large-scale distributed systems.
About
I’m an AI-focused software engineer who builds reliable solutions at the intersection of Generative AI and large-scale distributed systems. I’ve developed AI agents and automation tools that integrate with REST APIs, audit and improve developer workflows, and support production-grade evaluation pipelines for model quality and reliability.
I enjoy designing clean, testable systems—especially multi-tenant and infrastructure-heavy pipelines—using strong engineering practices like CI/CD, automated testing, and containerized deployments. From building ML inference pipelines that reduce latency to creating platforms for monitoring and triaging production model behavior, my goal is always the same: ship secure, highly reliable tools that create measurable business impact.
- 2 years' experience
- Joined September 2026
Industry experience
- Software & Internet
- Professional Services
Work experience· 4 roles
Developed a Python-based AI agent and automation tool that uses REST APIs to audit Azure DevOps workflows, identify incomplete tasks, and generate targeted user reminders. Implemented safety safeguards such as duplicate prevention and configurable business rules, while achieving 80% test coverage using network-isolated unit tests and automated linting. Focused on building secure and reliable tooling for operational workflow improvement.
Conducted LLM evaluation and model response grading by analyzing complex generative outputs to improve reasoning quality and technical accuracy. Executed task workflows to generate high-quality training data, directly enhancing the performance and reliability of core generative AI models.
Led multi-step intelligent system workflows that integrated predictive models into production services to automate operations and reduce manual processing time by 40%. Built and maintained scalable ML deployment infrastructure using FastAPI and Docker within a distributed microservices architecture, reducing API response times by 30%. Contributed to code reviews and upheld maintainability and testability for client-facing applications.
Education
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