I’m Siddhi Agrawal, a dedicated MLOps and Backend Engineer with 2.5+ years of experience deploying production-grade machine learning systems, building data infrastructure, and delivering scalable solutions using AWS, Python, and modern DevOps practices.
I offer complete, reliable, and maintainable MLOps and backend engineering services tailored to data-driven businesses and ML-focused startups.
What I Can Do for You:
- Model deployment using AWS SageMaker, Lambda, Step Functions
- CI/CD integration for ML pipelines using Jenkins, GitHub Actions
- Model versioning, tracking, and monitoring with MLflow, custom dashboards, or your choice of tools
- Automating ML lifecycle workflows end-to-end
Build production-ready APIs using Python, FastAPI
Secure, scalable architecture with Docker, role-based access, and logging
Real-time processing integration using Kafka, PostgreSQL, and S3
Seamless API integration with frontend or annotation tools
Design ETL pipelines using AWS Glue, ingesting and transforming large-scale data
Dataset versioning, lineage tracking, and train-test split enforcement via custom-built Data Registry
Optimized data architecture for analytics or machine learning needs
Why Choose Me:
I’ve led production deployments of large ML pipelines used in geospatial and analytics platforms
Winner of a company-wide AI hackathon (50+ teams) for building an LLM-powered data collection pipeline
Reduced operational costs by 50% and improved ML inference efficiency via real-time event-based architecture
Deep experience in cloud-native ML systems, including monitoring, security, and performance tuning
Strong communicator, mentor, and problem solver , I take full ownership from idea to deployment
Whether you need someone to deploy your ML model, build your backend infrastructure, or architect your data pipeline, I’ll help you do it right, reliably, securely, and with scalability in mind.
Let’s build something impactful together!