Senior Machine Learning Engineer | Dubai Or Bangalore Based.

DISCOVERED; Dubai; On-site; AED 300,000 - 550,000; Full time

Job summary

Forge is seeking an hands-on Machine Learning Engineer to own the classical ML powering the detection and triage layer for OT environments. You will build models that score alerts, classify assets, and separate real threats from noise, running locally on air‑gapped appliances.

You will design adaptive ML systems that learn on the edge, evolving over time based on traffic patterns and analyst feedback, and you’ll stand up a self‑hosted MLOps stack for experiment tracking and model management.

Qualifications

• 4+ years of hands-on experience building and shipping ML systems in production.
• Strong Python fluency with XGBoost, LightGBM, or CatBoost.
• Solid grounding in scikit-learn, tree ensembles, clustering, anomaly detection (Isolation Forests, autoencoders).
• Experience with online/incremental learning, concept drift, and active learning loops.
• Proficiency with ML pipelines and MLOps tools like Airflow, Kubeflow, DVC, MLflow.

Responsibilities

• Alert triage and scoring: build and tune gradient boosting models to rank security alerts and surface genuine incidents.
• Adaptive and online learning: design models that adapt over time with incremental learning and human-in-the-loop feedback.
• Asset and device classification: profile OT devices from raw network data using tree ensembles and clustering.
• Custom anomaly detection: implement Isolation Forests and autoencoders with OpenSearch for detecting unseen threats.
• Risk scoring: refine features to reflect real operational exposure.
• Production pipelines: create reproducible, versioned feature engineering and training pipelines.

Skills

Python

XGBoost

LightGBM

CatBoost

scikit-learn

Incremental learning

Tools

MLflow

OpenSearch

Airflow

Kubeflow

DVC

Dagster

Prefect

Job description

Job description

Machine Learning Engineer – Classical & Adaptive ML

Discover the Opportunity

We are hiring a hands‑on Machine Learning Engineer to own the classical ML powering Forge’s detection and triage layer. Forge protects critical Operational Technology (OT) and industrial control environments.

In this individual contributor role, you will build and tune models that score and prioritize security alerts, classify OT assets, and separate real threats from noise—all running directly on local, air‑gapped appliances.

Because every deployment operates in a unique, isolated OT environment without access to cloud MLOps services, our models can't be static artifacts shipped once. You will build adaptive machine learning systems that learn on the edge, evolving over time based on local traffic patterns and analyst feedback.

Discover the Role

• Alert Triage & Scoring: Build and tune XGBoost and gradient boosting models to rank security alerts, drastically cutting false positives and surfacing genuine incidents for analysts.

  
  

• Adaptive & Online Learning: Design models that adapt to individual site environments over time using incremental learning, concept drift detection, and human-in-the-loop feedback.

  
  

• Asset & Device Classification: Expand our Equipment Type Inference capabilities using tree ensembles and clustering to profile industrial devices from raw network fingerprint data.

  
  

• Custom Anomaly Detection: Implement Isolation Forests, one‑class methods, and autoencoders alongside our OpenSearch layer to catch threats that static rules miss.

  
  

• Risk Scoring: Refine features and models to ensure Forge’s risk scoring accurately reflects real operational exposure.

  
  

• Production Pipelines: Build reproducible, version‑controlled feature engineering, training, and evaluation pipelines that move beyond standard notebooks.

  
  

• Self-Hosted MLOps: Stand up and run a self-hosted MLflow setup for experiment tracking, model registry, and promotion of vetted models to production.

  
  

• On‑Device Model Lifecycle: Package models to run seamlessly within the appliance Docker stack, complete with evaluation gates, drift monitoring, and controlled on‑device retraining.

  
  

Discover the Requirements

• 4+ years of hands‑on experience building and shipping machine learning systems in production.

  
  

• Gradient Boosting Expertise: Strong Python fluency with deep experience in XGBoost, LightGBM, or CatBoost.

  
  

• Classical ML & Anomaly Toolkit: Solid grounding in scikit‑learn, tree ensembles, clustering, anomaly detection (Isolation Forests, autoencoders), and selective deep learning.

  
  

• Adaptive ML Experience: Practical experience with online/incremental learning, concept drift, and active learning/feedback loops.

  
  

• ML Pipelines & MLOps: Proficiency with pipeline orchestrators (Airflow, Prefect, Dagster, or Kubeflow), data/model versioning (DVC), and model tracking (MLflow).

  
  

• Imbalanced Data Expertise: Deep familiarity with rare‑event metrics (Precision, Recall, PR curves, calibration) rather than raw accuracy.

  
  

• Telemetry Data: Comfort working with large volumes of structured logs and network telemetry.

  
  

Discover the Desired

• Exposure to OT,ICS, or SCADA security, industrial protocols , or the Purdue model.

  
  

• Experience with OpenSearch or ES machine learning capabilities.

  
  

• Background deploying ...