About the Initiative

Our client are initiating a critical project to transform our current threat detection and threat hunting hypothesis capabilities. They have a functional core product that utilizes AI to identify threats, but to deploy it effectively at scale, we need to bridge the gap between "research code" and "production engineering."

The goal of is to professionalize our existing codebase, transitioning it into a robust, high-performance, **API-first** architecture. We are looking for an engineer who cares deeply about code quality, scalability, and state-of-the-art software practices to take this tool to the next level.

The Role

You will not just be writing code; you will be architecting the backbone of a system that automates threat hunting. Your primary focus will be optimizing data pipelines, enforcing software engineering rigor, and ensuring our AI models run efficiently on hardware.

Key Responsibilities:

• Code Professionalization: Refactor and enhance existing Python-based AI/ML modules into production-grade software.

• Design and implement data ingestion strategies that bridge legacy self-hosted clusters with modern cloud-native streaming services (transitioning from on-prem Kafka/Spark to managed cloud equivalents).

• Architecture Design: Implement an **API-first design** philosophy to ensure the product integrates seamlessly with our wider Cyber Defense ecosystem.

• Pipeline Optimization: Engineer robust big data pipelines (preprocessing, feature selection) to handle high-velocity security data.

• Performance Tuning: optimize code for specific hardware configurations (GPUs) to ensure low-latency threat detection.

• Quality Assurance: Establish comprehensive testing frameworks (unit, integration, regression) and CI/CD pipelines.

Technical Requirements

We are looking for a strong software engineer with an interest in AI. You do not need to be a Data Scientist, but you must be an expert in building systems that support Data Science.

Core Engineering & Languages

• Python Mastery: Extensive experience with Python, specifically regarding AI/ML modules (e.g., `sklearn`, `PyTorch`).

• Low-Level Languages: Competence in **Rust, C++, or Go** is highly desirable for optimizing performance-critical components.

• Best Practices: A fanatic for clean code, testing methodologies, and maintainable software architecture.

Data Infrastructure & Stack

• Big Data Pipelines: Experience building and maintaining data ingestion and preprocessing workflows.

Tech Stack Hands-on experience with:

• Vector Databases: Qdrant

• Messaging/Streaming: Kafka

• Caching/Storage: Redis

• ML Frameworks: Torch

Infrastructure & Hardware

• Cloud Native: Basic knowledge of containerization and orchestration (Docker, Kubernetes, Helm). GCP and AWS is a plus.

• Hardware Acceleration: Basic understanding of AI/ML hardware interactions, specifically GPUs and CUDA programming.

The "Nice-to-Haves

• AI Knowledge: Deep theoretical knowledge of AI is **optional**. However, a willingness to learn about the specific AI models we use during the project is required.

• Cybersecurity Domain: Familiarity with threat hunting or SOC operations is a plus, but not a blocker.

Why Join This Project?

• Your code will directly enhance the Cyber Defense Center’s ability to detect novel threats.

• Great opportunity for a pure Software Engineer to gain exposure to AI/ML workflows and GPU computing.

Questions:

If you have any questions regarding the assignment, please contact the responsible recruiter via e-mail: Julijana Stojanovic; [julijana.stojanovic@eworkgroup.com]