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
(ref:hirist.tech)
