Freelance Opportunity -Machine Learning Engineer (MLE) | Personalization & Recommendation Systems

Company Description ThreatXIntel is a growing Cybersecurity, IT Staffing, and Consulting company delivering end-to-end technology and security solutions. Our services include cloud security, web and mobile application security testing, DevSecOps, vulnerability assessments, IT consulting, and professional staffing services.We support global corporate clients by hiring and deploying skilled professionals across IT and cybersecurity domains while helping organizations strengthen security, optimize operations, and scale efficiently. ThreatXIntel is committed to enabling business growth through secure, reliable, and high-quality technology solutions.Job Description :We are looking for a highly skilled Machine Learning Engineer to support large-scale personalization initiatives powering digital and in-store customer experiences for a leading retail enterprise.This is a hands-on engineering-focused role ideal for someone who enjoys building production-grade ML systems at scale — beyond just model development. Key Responsibilities :• Design and build end-to-end ML pipelines for recommendation and personalization systems• Develop scalable feature engineering pipelines (batch + real-time)• Build and optimize online/offline feature stores for low-latency inference• Work with Kubeflow, Dataproc, and distributed systems for ML workflow orchestration• Develop real-time inference systems for high-throughput predictions• Implement model monitoring, drift detection, and automated retraining pipelines• Collaborate with platform and data engineering teams to ensure scalable data infrastructure• Optimize ML system performance, latency, scalability, and reliability Required Skills :✔ GCP (Vertex AI, Dataproc, BigQuery)✔ Kubeflow Pipelines✔ Python✔ PySpark / Apache Spark✔ Feature Store Architecture✔ Real-time / Streaming ML Systems✔ TensorFlow / PyTorch or similar frameworks✔ Distributed ML system design & production deployment Ideal Candidate :• Strong experience building production ML systems end-to-end• Deep expertise in large-scale feature engineering• Experience with real-time or near real-time ML applications• Strong understanding of scalable ML infrastructure and orchestration

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