Freelance – AWS Data

Company Description ThreatXIntel is a dynamic startup specialized in cyber security, providing innovative and affordable solutions to protect businesses and organizations from various cyber threats. Our team offers services such as cloud security, web and mobile security testing, DevSecOps, and more, tailored to meet the unique needs of clients. Committed to proactive security practices, we monitor and test digital environments to identify vulnerabilities before they can be exploited. With a mission to deliver peace of mind, we empower businesses to focus on their growth while ensuring their digital assets remain protected. ThreatXIntel is dedicated to providing top-tier cyber security services to organizations of all sizes. Role Description We are seeking a Freelance AWS Data & ML Engineer to support and enhance our cloud environment following the migration from GKP (legacy internal cloud platform) to AWS. Migration is complete, and the current focus is on stabilizing, optimizing, and expanding workloads across AWS services, ML pipelines, and LLM-enabled use cases. The ideal candidate will have strong hands-on skills across AWS data services, Terraform, PySpark, and machine learning model operations. Key Responsibilities - Provide ongoing support for AWS workloads, including EMR, ECS, and EAC, ensuring smooth operation and monitoring of migrated services. - Maintain, optimize, and troubleshoot distributed data processing jobs built on PySpark and running on AWS EMR. - Support, deploy, and monitor production-grade ML models across domains such as card, auto, and retail. - Assist in scaling and integrating LLMs into existing application workflows or new service components. - Manage and optimize DynamoDB tables, including indexing, performance tuning, and data modeling. - Develop and maintain Infrastructure-as-Code using Terraform for AWS services, ensuring consistency and automated deployments. - Write and maintain clean, efficient, and scalable Python code to support data workflows, ML pipelines, APIs, and automation tasks. - Collaborate with data scientists, ML engineers, DevOps teams, and business stakeholders to ensure reliability, performance, and continuous improvement of cloud workloads. - Participate in root cause analysis, troubleshooting sessions, and production support activities. - Document processes, architecture, best practices, and operational runbooks for long-term maintainability. Required Skills & Qualifications - Strong hands-on experience with AWS EMR, ECS, and cloud-native compute services (EAC or equivalent). - Experience supporting, deploying, or retraining ML models (card, auto, retail, or similar high-volume enterprise models). - Knowledge of LLM integration, prompt workflows, or API-based inference pipelines. - Proficient in DynamoDB data modeling, indexing patterns, and performance optimization. - Hands-on experience building and maintaining AWS infrastructure using Terraform. - Strong programming experience in Python, including data scripting, backend utilities, and ML pipeline implementations. - Extensive experience with PySpark for distributed data processing on EMR or Spark-based platforms. - Experience working with cloud-based ETL/ML environments, CI/CD practices, and production troubleshooting. - Ability to work independently, communicate clearly, and collaborate across multiple teams in a fast-moving environment. Ideal Candidate Profile - Comfortable supporting a large AWS environment post-migration. - Strong problem-solver with the ability to troubleshoot across data, ML, and cloud infrastructure. - Hands-on engineer who can balance quick fixes with long-term scalable improvements. - Strong documentation skills and attention to detail.