Job Title

Company Description ThreatXIntel is a startup cyber security company dedicated to protecting businesses and organizations from cyber threats. The company offers a range of services, including cloud security, web and mobile security testing, cloud security assessment, and DevSecOps. ThreatXIntel delivers customized, affordable solutions to meet the specific needs of clients, focusing on proactive security measures to identify vulnerabilities before exploitation. Role Description We are looking for a highly skilled Freelance AI/ML Engineer or Technical Lead with proven experience building and deploying scalable machine learning applications using Amazon Bedrock , traditional ML techniques, and AWS-native services. This is an engineering-focused role , not a data science position, and requires hands-on experience developing and maintaining ML systems in production. You will collaborate with cross-functional teams to integrate large language models (LLMs), optimize traditional ML pipelines, and drive real-world impact through robust AI solutions. A strong foundation in statistics, regression modeling, and prompt engineering is required. Key Responsibilities Design, build, and deploy ML solutions using Amazon Bedrock and foundation models (Claude, Titan, Llama2, Mistral, Jurassic, etc.) Integrate and fine-tune LLMs for business-specific tasks, including prompt engineering and evaluation Develop traditional ML models (e.g., linear/logistic regression, decision trees, ensemble methods) using Python and ML libraries (scikit-learn, TensorFlow, PyTorch) Apply statistical methods for model validation, evaluation, and tuning Build and maintain ML pipelines using AWS services such as SageMaker, Lambda, API Gateway, S3, and IAM Design CI/CD workflows for ML deployments , including versioning, monitoring, and rollback strategies Collaborate with software engineers, data engineers, and business stakeholders for end-to-end ML lifecycle implementation Integrate Bedrock into custom APIs and analytics layers for both real-time and batch use cases Provide ongoing post-deployment support , debugging, and performance optimization Mentor junior team members and lead architectural decisions for GenAI implementation on AWS Contribute to analytics dashboards, reporting pipelines, or visualization layers where applicable Required Skills and Experience 5+ years of experience in Machine Learning Engineering , with end-to-end production deployment experience Hands-on expertise with Amazon Bedrock and foundation models such as Claude, Titan, Llama2, Mistral Strong skills in Python , with experience in ML frameworks: scikit-learn, TensorFlow, or PyTorch Experience in Prompt Engineering , LLM integration, and Bedrock model configuration Solid understanding of regression modeling , statistical analysis, and evaluation metrics AWS proficiency with services including SageMaker , Lambda , API Gateway , S3 , and IAM Familiarity with MLOps practices , including CI/CD, model versioning, and monitoring Strong debugging and post-production support skills for ML deployments Experience working with real-world, high-scale AI/ML use cases in enterprise environments Nice to Have Experience with RAG (Retrieval-Augmented Generation) architectures Familiarity with vector databases such as Pinecone or FAISS Experience in containerized or serverless orchestration for ML systems AWS ML Specialty or Solutions Architect certifications Tableau or other data visualization experience Prior deployment experience in healthcare , finance , or regulated industries