AI/ML Engineer — Bengaluru, IN

Shrey Singh

I build ML systems that have to actually run — forecasting pipelines, agentic workflows, and the infrastructure underneath them.

Currently MLOps Engineer at Agneyas Labs, building an internal AutoML tool and a physics-informed digital twin for battery life-cycle observability. Previously ML intern at NetApp, on revenue forecasting for $150M+ quarterly planning.

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I15 projects

Systems, not filler.

Select any project for the problem, system design, and evidence behind it.

IIAgneyas Labs · NetApp

Where the work has shipped.

2026 — Present

Machine Learning Ops Engineer Agneyas Labs

Bengaluru

  • Building an internal AutoML tool that standardises preprocessing, model search, and evaluation across pipelines.
  • Developing a physics-informed neural network digital twin for battery life-cycle degradation and state-of-charge observability.
PythonPyTorchPINNsMLOps
2025 — 2026

Machine Learning Intern NetApp

Bengaluru

  • Built a one-click forecasting pipeline supporting $150M+ in quarterly revenue planning across 1.5M+ records.
  • Lifted commission forecast accuracy 15% for 4,900+ sales reps with XGBoost ensembles — MAPE down to 6–10% on 60+ engineered features.
  • Added cold-start logic, quantile forecasts, and scenario modelling while cutting the end-to-end run from 8 hours to 2.
PythonXGBoostTime seriesBatch ML
IIIToolkit

The stack, without the logo wall.

Languages
Python, SQL, Go, TypeScript, Java, C++, Rust
ML / AI
PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, pandas, NumPy
GenAI / Agents
LangGraph, LangChain, RAG, pgvector, Gemini API, OpenAI API, Pydantic
Backend / Data
FastAPI, Next.js, React, PostgreSQL, Redis, SQLite
Infra / MLOps
Docker, GitHub Actions, MLflow, DVC, Prometheus, Grafana, Vercel
IVContact

Have a hard system worth building?

Currently building at Agneyas Labs. Always happy to talk ML systems, agentic products, and backend infrastructure — especially the parts that have to survive production.

9shrey@gmail.comLinkedInResume