Description :

About the Role

This role focuses on building real-time ML decision engines that power alerts, risk checks, feed scoring, and portfolio intelligence.

You will work on systems that decide, score, suppress, and trigger actions - under latency, reliability, and trust constraints.

Key Responsibilities :

- Build ML models for classification, scoring, and anomaly detection

- Design offline + online feature pipelines

- Combine rules, heuristics, and ML into deterministic systems

- Deploy and monitor ML services in production

- Handle false positives, model drift, and noisy real-world data

Required Skills :

- Experience building or working with decision, ranking, or scoring systems

- Strong understanding of experimentation and evaluation metrics

- Backend engineering fundamentals (Python / APIs / services)

- Experience deploying models behind Flask or Fast APIs

- Ability to reason about negative signals and suppression

- Experience working with sparse, incomplete, or imperfect data

Good to Have :

- Feed, content, or recommendation systems experience

- Embedding or NLP-based ranking exposure

- Large-scale experimentation or A/B platforms

What We Value :

- Decision quality over model complexity

- Engineers who own outcomes, not notebooks

- Comfort debugging ML behavior in production