Position Overview
Department: Engineering - ML & Data Science
Role type: Full-Time, On-site
Experience: 3-6 Years (Mid-Senior)
Location: Gurgaon, HR
About the Role
This is the core technical role. You will own the full ML pipeline - from raw inverter and weather
time-series data to production-grade models that guarantee solar generation, predict inverter
failures 7-14 days in advance, and detect anomalies in real time. We focus heavily on traditional
machine learning built elegantly and robustly as per our requirements (XGBoost, LightGBM,
Prophet, Isolation Forest) to ensure fast inference and strong accuracy. The right model for the
job, shipped reliably, is what matters.
What You'll Own
● End-to-end ML pipeline: data ingestion from ClickHouse → feature engineering →
training → evaluation → MLflow registry → FastAPI inference API
● Generation model: compute and track Performance Ratio (PR) for each site, detect
underperformance vs GHI-based expected yield with ±5% accuracy
● Anomaly detection: Isolation Forest (phase 1) → LSTM autoencoder (phase 2) on MPPT
power ratio, inverter temperature trend, fault frequency
● Predictive maintenance: Remaining Useful Life (RUL) model on inverter temp + fault
code history - 7-14 day failure prediction horizon
● Yield forecast: LightGBM / XGBoost model using Solcast/Open-Meteo GHI forecasts +
historical PR baseline to predict weekly kWh ± 8%
● MLOps: weekly automated retraining pipeline, model versioning in MLflow, A/B model
promotion logic, performance drift detection
● Feature engineering from raw time-series: rolling averages, sin/cos time encoding,
weather transposition (GHI → POA), lag features, string imbalance ratios
● Monthly automated report generation: actual vs forecast, PR trend, maintenance log,
CO₂ offset
Required Skills
● Python 3.10+ - production-quality code, not just notebook scripts. OOP, packaging,
testing.
● Traditional ML architecture - XGBoost, LightGBM, scikit-learn. Strong intuition for
structured time-series datasets.
● Time-series analysis - seasonality decomposition, stationarity, Prophet, statsmodels, lag
features
● Anomaly detection - Isolation Forest, Z-score / IQR, CUSUM, statistical process control● Feature engineering - rolling windows, ratios, normalisation, encoding cyclical time
features
● MLflow or equivalent - experiment tracking, model registry, artifact management
● FastAPI or Flask - wrapping models in inference APIs that backend engineers can call
● SQL - complex queries on ClickHouse or PostgreSQL for feature extraction from time-
series
● Pandas, NumPy - confident data manipulation at scale (millions of rows)
● Git - version control for notebooks and production code
Bonus Skills (Strong Plus)
● Solar domain knowledge - Performance Ratio, specific yield, CUF, irradiance
transposition models (Perez, Hay-Davies)
● Survival analysis / RUL modelling - Weibull distribution, Cox proportional hazards,
degradation models
● LSTM autoencoder for anomaly detection - implementation experience, not just
awareness
● ClickHouse - time-series query patterns, MergeTree engines, materialised views for
feature computation
● Kafka consumer in Python - reading from Kafka topics for online feature computation
● AWS SageMaker, Vertex AI, or any managed ML platform - training job orchestration
● NILM (non-intrusive load monitoring) - useful for future load disaggregation features
● Elixir, Go, or Rust - reading code from these languages for pipeline integration
What Good Looks Like in This Role
● Day 30: PR pipeline computing daily for each site; baseline anomaly model live with
<10% false positive rate
● Day 90: MPPT imbalance detection live; inverter temp trend model with 7-day prediction
horizon
● Day 150: All 4 models in production; weekly retraining cron running; monthly PDF report
auto-generating
● Day 365: LSTM anomaly upgrade live; yield forecast MAE <8%; RUL model validated on
6+ months of fault history
You'll Thrive Here If You
● Prefer simple, explainable models that work over complex models that sometimes work
● Are rigorous about evaluation - you set up proper train/val/test splits on time-series data
(no data leakage)
● Understands that a model in a Jupyter notebook is not a model in production
● Communicate uncertainty clearly - you know when your model is confident and when it
isn't
● Are self-directed - you can take 'we need to predict failures' and figure out the data, the
model, and the metric
What You Get
● Ownership of the entire ML stack - from data to production inference
● Real, novel problem: generation on time-series IoT data is not a solved problem
● Work directly with the founding team - your architectural decisions shape the product
Pay: ₹600,000.00 - ₹1,000,000.00 per year
Work Location: In person
