MLOps & Backend Engineer | Scalable ML Pipelines • Cloud Infrastructure • AWS • Automation Expert
Hi, I’m Siddhi Agrawal, a passionate MLOps and Backend Engineer with 2.5+ years of experience designing and deploying scalable, production-ready machine learning systems.
I currently work at EagleView, where I lead the deployment of ML pipelines using AWS services like SageMaker, Lambda, Step Functions, and S3. I also build end-to-end backend APIs with FastAPI and Python, integrating them into CI/CD pipelines powered by Jenkins and Docker.
My expertise lies in automating the ML lifecycle, managing data transformations at scale, and building traceable infrastructure that supports experimentation and monitoring. I’ve created robust ETL pipelines with AWS Glue, built custom Data Registries for versioned datasets, and designed architectures to prevent data leakage and ensure clean training pipelines.
I’m especially strong at bridging the gap between machine learning and production-grade software — combining my understanding of ML workflows with hands-on backend and DevOps skills. Whether it's performance tuning, system design, or streamlining APIs for ML models, I bring a problem-solving mindset and full-stack ownership to every project.
Highlights:
- Built a geospatial data collector powered by LLMs, SAMv2, and SageMaker, won 1st place in a company-wide hackathon with 50+ teams
- Reduced data pipeline processing time by 65% and operational costs by 50%
- Led benchmarking and failure case analysis, improving model yield by 9%
- Strong communication, code quality, and mentoring practices
If you’re looking for a reliable partner to build, automate, or scale your ML systems or backend services, I’d love to work with you!
Work Terms
Availability: Open to short-term and long-term projects.
Working Hours: 8:00 PM - 2:00 AM IST (Flexible for global clients)
Communication: Prefer chat via Guru, email, or Zoom/Google Meet for regular sync-ups
Payment Terms: Based on project milestones or hourly billing as agreed
Deliverables: Well-documented, modular, and maintainable code; includes testing and deployment support
Turnaround: Clear and responsive updates with regular progress tracking
I believe in transparent communication, reliable delivery, and building solutions that scale. Whether it’s automating your ML pipeline or deploying a performant backend API, I aim to exceed expectations.