Freelance opportunity: ai/machine learning engineer

Company Description Threat XIntel is a startup specializing in providing cybersecurity solutions tailored to protect businesses and organizations from various cyber threats. With expertise in cloud security, web and mobile security testing, cloud security assessments, and Dev Sec Ops, Threat XIntel is dedicated to delivering customized, affordable services. We prioritize proactive approaches, monitoring and addressing vulnerabilities to safeguard the digital environments of our clients. Our mission is to empower businesses of all sizes with top-notch cybersecurity, enabling them to focus on growth and innovation with confidence and peace of mind. Role Description We are seeking a highly skilled Freelance AI/Machine Learning Engineer with strong experience in ML engineering, MLOps, and cloud-native data pipeline development. The ideal consultant will have deep expertise in Python, AWS cloud services, ML model deployment, and end-to-end machine learning lifecycle management. This role involves designing scalable ML pipelines, productionizing models, performing exploratory analysis, and ensuring high-quality data workflows. Key Responsibilities Design, develop, and optimize complex data pipelines using ML engineering best practices for scalability and performance. Build and maintain MLOps pipelines to support model deployment, monitoring, retraining, and lifecycle management. Implement ETL/ELT workflows using AWS services (Glue, Lambda, Step Functions, S3, EMR, etc.). Develop end-to-end machine learning pipelines including data ingestion, feature engineering, model training, and inference. Work closely with data scientists to validate datasets, refine features, and ensure ML-ready data assets. Conduct exploratory data analysis (EDA) on raw data sources, identify anomalies, and derive actionable insights. Track data lineage , perform root cause analysis, and resolve data quality or pipeline issues. Use AWS Sage Maker and other ML platforms to build, deploy, and manage production ML systems. Monitor models in production, investigate alerts, and develop scripts/tools for ongoing model health checks. Collaborate with cross-functional teams to translate business processes into scalable AI/ML solutions. Follow best practices across software engineering—testing, version control, CI/CD, security, and documentation. Essential Skills Bachelor’s degree required; Master’s degree preferred. 7+ years of experience in AI engineering, ML engineering, or data engineering. 3+ years of hands-on experience building ETL pipelines using AWS cloud services . Proven experience designing ML pipelines for production deployment and monitoring . Strong proficiency in Python and ML frameworks (Tensor Flow, Py Torch, scikit-learn, etc.). Hands-on experience with AWS Sage Maker or similar ML platforms. Solid grasp of software engineering principles : design patterns, testing, security, version control. Strong understanding of Machine Learning Development Lifecycle (MDLC) and MLOps best practices. Experience building end-to-end ML architectures from design through deployment. Nice to Have Experience with Kubernetes, Docker, or container orchestration. Experience with feature stores, data quality tools, or ML observability platforms. Knowledge of distributed systems (Spark, Ray, Dask) is a plus. Ideal Candidate Profile Strong technical depth across ML, data engineering, and cloud platforms. Ability to work independently and deliver production-ready solutions. Excellent problem-solving skills with attention to detail. Clear communicator with strong documentation and stakeholder-management skills.