Machine Learning Practitioner & Backend Systems Engineer
I work at the convergence of AI systems, backend engineering, and cloud infrastructure, building practical, scalable solutions that move from idea to deployed product. My work spans LLM agents and RAG architectures to high-performance APIs that reliably serve ML models in production. Designing clean systems to ship iteratively and scale with intent. Everything I build is rooted in engineering fundamentals and guided by a product-focused mindset.
Deep Learning • LLMOps • RAG Pipelines • Computer Vision
Backend APIs • Cloud-Native Architecture • System Design
Multi-Agent Systems • Model Serving • Vector Search
Languages: Python, TypeScript, C++, SQL AI & ML: PyTorch, Hugging Face Transformers, OpenCV, LangChain, LlamaIndex, RAG, Agentic AI Backend & Databases: FastAPI, NestJS, API Design, PostgreSQL, Redis, Milvus & Pinecone Cloud & Infrastructure: AWS (EC2, S3, EKS, Lambda, Bedrock), GCP (GKE, BigQuery) DevOps & MLOps: Docker, Terraform, GitHub Actions, MLflow, Airflow, Prefect, Weights & Biases Observability & Systems: Prometheus, Grafana, OpenTelemetry, Sentry, Microservices, gRPC, Event-Driven Architecture
I enjoy building systems to production state: architecting services, developing ML workflows, and deploying everything through cloud-native pipelines. So my main focus remains consistent across projects:
- Reliability: Systems that hold up under real usage
- Scalability: Infrastructure that grows without friction
- Maintainability: Clear, modular, testable code
- Performance: Faster inference, optimised retrieval, efficient design
- Impact: Practical solutions powered by modern AI
Whether it's a microservice architecture or a vector-search pipeline, I aim for clarity, robustness, and long-term maintainability.
Email: harshilmakhija@outlook.com
LinkedIn: https://www.linkedin.com/in/harshil-makhija-500909353/
X / Twitter: https://twitter.com/MakhijaHarshil
Always learning. Always building.