Information Technology Technical Support

Fortress Digital Services; Jakarta Selatan; On-site; IDR 180,000,000 - 360,000,000; Full time

Job summary

Fortress Digital Services seeks an AML data and ML engineer to monitor, troubleshoot, and maintain AML applications and transaction monitoring systems. You will analyze AML data using SQL/Python and support ML models, rules, and risk detection, collaborating with AML/Compliance and IT teams.

The role requires at least 1 year of ML/AI experience, proficiency in Python and ML frameworks, and familiarity with REST APIs, Flask/FastAPI, and Docker/Kubernetes.

Qualifications

• Proficiency in Python and ML frameworks including PyTorch, TensorFlow, and Scikit-Learn.
• Strong knowledge of ML and DL algorithms, preprocessing, and feature engineering.
• Experience with SQL and large-scale data manipulation using Pandas/NumPy.
• Familiarity with model deployment via REST APIs (Flask/FastAPI) and containerization (Docker/Kubernetes).
• Nice to have: LLMs/Generative AI, cloud services (AWS/GCP/Azure) or MLOps tools (MLflow/Kubeflow).

Responsibilities

• Monitor, troubleshoot, and maintain AML applications and transaction monitoring systems.
• Analyze AML data using SQL/Python and support ML models, rules, and risk detection.
• Support system testing, deployment, integration, and collaboration with AML/Compliance and IT teams.

Skills

Python

SQL

Pandas

NumPy

ML Frameworks

ML & DL algorithms

REST APIs

Docker/Kubernetes

LLMs / Generative AI

Tools

PyTorch

TensorFlow

Scikit-Learn

Flask

FastAPI

Docker

Kubernetes

Job description

• Monitor, troubleshoot, and maintain AML applications and transaction monitoring systems.
• Analyze AML data using SQL/Python and support ML models, rules, and risk detection
• Support system testing, deployment, integration, and collaborate with AML/Compliance and IT teams.

Requirement:

• Minimum 1 year of relevant work experience in Machine Learning / Artificial Intelligence.
• Proficiency in Python and Machine Learning frameworks such as PyTorch, TensorFlow, and Scikit-Learn.
• Strong understanding of Machine Learning and Deep Learning algorithms, including data preprocessing and feature engineering.
• Experience with SQL and large-scale data manipulation using Pandas and NumPy.
• Familiarity with Machine Learning model deployment, including REST APIs, Flask/FastAPI, and Docker/Kubernetes.
• Preferred: Experience with LLMs/Generative AI, Cloud Services (AWS/GCP/Azure), or MLOps tools (MLflow/Kubeflow).
• Strong problem-solving and analytical skills, with good communication and teamwork abilities.