ML Engineer (Quantum)

• Concepts: qubits, superposition, measurement, Bloch sphere, single/two-qubit gates, circuits.
• Parameterized circuits (VQCs): building, initializing, and training with PyTorch.
• Core algorithms: QAOA, VQE, quantum kernels (QSVM), QCNN at a basic level.
• Noise & NISQ reality: shots, error rates, mitigation basics; sim vs real hardware trade-offs.
• Transpilation & resources: depth, width, fidelity, run-time/credit estimation.
• Tooling: Qiskit or PennyLane (one solid), plus ability to run on IBM/ IonQ simulators; MLflow/W&B for reproducibility.

Nice-to-have

• OR/optimization (OR-Tools), graph problems; OCR/NLP pipelines; pgvector/FAISS.
• Basic Docker & FastAPI to expose prototypes.

What you’ll do

• Clean & prep data (SQL, Pandas/Polars); build baseline ML (sklearn/XGBoost, PyTorch).
• Prototype small QML pilots (Qiskit/PennyLane): simple variational circuits, quantum kernels.
• Compare classical vs hybrid results; document metrics, costs, and trade-offs.
• Package models (notebooks → FastAPI/batch), write tests, and monitor basics.

Must-haves

• 1–2 yrs in ML (or strong projects), solid Python, SQL, Git.
• Hands-on with sklearn + at least basic PyTorch.
• Exposure to Qiskit or PennyLane and parameterized circuits (coursework/projects okay).
• Clear communication and reproducible work (MLflow/W&B or similar a plus)

Job Types: Full-time, Permanent, Fresher, Internship
Contract length: 6 months

Pay: From ₹10,000.00 per month

Work Location: In person