Role : Sr Machine Learning Engineer Location : Dallas Tx Experience : 10+ years JD : Key Responsibilities: Design and implement statistical and ML models (e.g., linear regression, logistic regression, decision trees, ensemble methods) for business-critical applications. Perform exploratory data analysis, feature engineering, and data preprocessing using best statistical practices. Build and deploy scalable ML pipelines using AWS-native services such as SageMaker, Lambda, S3, and API Gateway. Ensure robust model evaluation, testing, and performance tuning using cross-validation, ROC/AUC, and other statistical metrics. Collaborate with software/data engineering teams to integrate models into production systems. Maintain clean, efficient, and well-documented ML code with CI/CD and MLOps best practices. [Optional] Contribute to GenAI use cases if relevant, leveraging Amazon Bedrock or similar platforms for LLM-powered components. Required Skills & Qualifications: 6+ years of hands-on ML engineering experience, preferably in production environments. Proven expertise in statistical modeling and regression analysis. Hands-on experience with Amazon Bedrock. Strong programming skills in Python with frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch. Experience with AWS ML services: SageMaker, Lambda, S3, API Gateway, etc. Solid understanding of statistical techniques such as hypothesis testing, sampling, time-series forecasting, PCA, etc. Exposure to MLOps workflows, including model monitoring, versioning, and automation pipelines. Excellent problem-solving, debugging, and communication skills. Nice to Have: Experience with Prompt engineering, or LLM-based architectures. Familiarity with RAG (Retrieval-Augmented Generation) or vector databases like Pinecone/FAISS. AWS ML Specialty Certification or equivalent.