AI/ML Expert for Cybersecurity Startup

@kamran2012

Hi there, I will architect and build ZEX's core intelligence layer — scam detection via NLP, phishing URL classification, and UPI fraud detection through behavioral anomaly models — while mentoring your mid-level developer through code reviews and architecture decisions. For the real-time threat pipeline, I will structure each model behind FastAPI microservices with async inference and feature caching so latency stays low under production load. For scam detection specifically, a fine-tuned transformer on Indian-language SMS and WhatsApp data will outperform keyword-based approaches significantly — multilingual BERT variants handle Hinglish and code-switched text well, which is critical for India-focused fraud. Looking forward to talking through the details. Kamran

@avinashdubeyg209

Image and pattern recognition (deep learning) Threat analysis and prediction (machine learning) Collaborate with data scientists to refine algorithms Ensure system scalability and performance Integrate AI models with the existing cybersecurity infrastructure Provide technical mentorship to junior developers Requirements Proven experience in Python development, particularly with AI/ML projects Strong background in NLP, deep learning (TensorFlow/PyTorch), and machine learning algorithms Familiarity with cybersecurity challenges, especially fraud and scam detection Excellent problem-solving skills and ability to work in a fast-paced startup environment Experience with cloud services (AWS, Google Cloud, etc.) preferred Strong communication skills and team-oriented mindset What We Offer Opportunity to work on cutting-edge technology in the rapidly growing field of cybersecurity Flexible working hours and the option to work remotely Competitive hourly rate, commensurate with experience Collaborative and innovative work culture To apply, please send your resume, portfolio, and a brief cover letter outlining your relevant experience to [

@himanshus642

Hi, This aligns well with my experience building production ML systems and real-time APIs. Experience: * Designed end-to-end ML pipelines (data → training → deployment) * Built NLP systems (text classification, spam/scam detection) * Developed anomaly detection models for fraud/use-case patterns * Deployed low-latency APIs using FastAPI with Docker-based setups Approach: * Define modular ML architecture (data pipelines, model layer, API layer) * NLP models for SMS/WhatsApp/email scam detection * URL/phishing detection using classification + heuristics * Fraud detection using behavioral/anomaly models * Real-time inference APIs (FastAPI) with optimized latency * Containerized deployment (Docker, scalable infra ready) ✔ Focus: * Production-ready systems * Clean, scalable architecture * Measurable model performance ✔ Collaboration: * Code reviews and guidance for your developer * Structured development and iteration Availability: Ready for ongoing hourly engagement Quick question: Do you already have labeled datasets, or should I help design the data collection and labeling pipeline as well? I can take ownership of the ML layer and help build a reliable intelligence system for ZEX.

@Visharaa2024

Hi, I understand that your goal with ZEX is to build a production ready, real-time AI/ML intelligence layer focused on fraud detection, scam prevention, and threat intelligence, particularly tailored for the Indian ecosystem. This involves designing a scalable end-to-end architecture that can process data from SMS, WhatsApp, emails, detect phishing and malicious URLs, and identify behavioral anomalies in transactions like UPI, all with high accuracy and low latency. To achieve this, I would use Python with Fast API for building high performance ML APIs, and leverage frameworks like TensorFlow for model development, including transformer based NLP models for scam detection and anomaly detection techniques for fraud analysis. I would also set up robust data pipelines, training workflows, and evaluation systems using tools like Pandas and Airflow, and handle deployment using Docker and Kubernetes to ensure scalability and reliability. Alongside development, I will take ownership of the system architecture, guide your existing developer through code reviews and best practices, and ensure the final solution is clean, maintainable, and truly production-grade rather than experimental.

@katxarra

⭐⭐⭐⭐⭐ Production AI/ML Architecture for ZEX Cybersecurity ⭐⭐⭐⭐⭐ Hi there! I understand ZEX needs production-ready intelligence systems, not simple ML experiments. I can help architect and build Python AI/ML pipelines for scam NLP detection, phishing URL detection, UPI fraud/anomaly detection, real-time FastAPI/Flask ML APIs, model evaluation, Docker/Kubernetes deployment, and low-latency optimization. I can also guide your mid-level developer with architecture, code reviews, and clean engineering practices. My focus is simple: accurate models, scalable APIs, strong security logic, and a system ready for real users.

@ViktorUmanskyi

As a seasoned backend-focused Python developer with a strong track record of building scalable, API-driven platforms and robust architectures, I'm confident in my ability to deliver on the ambitious goals of your cybersecurity startup. While I might not have specific experience in AI/ML like Scikit-learn and PyTorch, my deep familiarity with Python, FastAPI/Flask, and my adaptability in familiarizing myself with new technologies enables me to make solid contributions. My approach to development aligns well with the vision for ZEX - production-ready systems built for scale. I don't just write code; I architect solutions that are clean, reliable, predictable, and high-performing under heavy loads. Furthermore, my understanding of NLP techniques can be beneficial when detecting fraudulent activities from various sources like SMS, WhatsApp, and emails. Having experience deploying applications using Docker/Kubernetes, I understand the importance of efficient deployment for scalability and performance. Plus, having worked closely with frontend teams in past projects, I understand how to transform your requirements into feature-rich code that is ready for real-world use. In summation: my solid backend background in Python, combined with a capacity to generate clean code and an aptitude for scaling codebases can provide invaluable contributions for ZEX's core intelligent systems. Let's take the next step together towards building a more secure digital environment!

@teerthofficial

Hello there, I’ve carefully reviewed your project details and fully understand your requirements. I’m confident that I can deliver high-quality results that meet your expectations within the given timeframe. I’d be happy to discuss your project further and get started right away. Best regards, Thanks

@karthikresonite

Hi, Resonite Technologies here — we have a senior AI/ML team with strong experience in **fraud detection, NLP, and real-time ML systems**, and can lead the architecture for ZEX end-to-end. ✔ How we’ll approach ZEX: • Design scalable ML architecture (data → training → inference → monitoring) • Build models for: – Scam detection (SMS/WhatsApp/email using NLP + transformers) – Phishing & malicious URL detection – UPI fraud & behavioral anomaly detection • Develop **low-latency ML APIs (FastAPI)** for real-time scoring • Create data pipelines, feature stores & evaluation workflows • Optimize for accuracy + sub-second inference • Deploy via **Docker + Kubernetes** (production-grade) • Mentor your mid-level dev (code reviews, architecture guidance) ✔ Our experience: • Built AI systems for fraud detection, anomaly detection & risk scoring • Expertise in **Python, PyTorch, TensorFlow, Scikit-learn** • Real-time ML pipelines with Kafka, Redis, and streaming data • Production deployments with monitoring, retraining & CI/CD ✔ What you get: • Clean, scalable, production-ready ML systems • Clear architecture + documentation • Strong ownership from design to deployment ✔ Availability: Immediate start | Flexible hourly engagement We can also help define datasets, labeling strategy, and India-specific threat patterns. Let’s build a robust intelligence layer for ZEX ? — Karthik Resonite Technologies