Machine Learning Jobs in Plano, TX

Summer internship working on applied data science projects (e.g., anomaly detection, numerical optimization, PII/PHI NLP). Responsibilities include end-to-end model development, writing modular Python code, using Linux and development tools, containerization and deployment basics, participating in code reviews and agile rituals, and clearly presenting results to technical and non-technical audiences.

Develop and deploy end-to-end ML solutions for smart manufacturing: build data pipelines, feature engineering, CV and LLM-based models (RAG/agentic workflows), prototype and productionize models, implement monitoring/retraining, and collaborate with cross-functional teams using modern development and CI/CD practices.

Design, develop, test, and support scalable AI/ML-powered enterprise applications and microservices. Implement LLMs, Generative AI, and RAG solutions; build RESTful APIs, automated tests, CI/CD pipelines; troubleshoot production issues and collaborate in Agile teams.

Lead end-to-end AI/ML architecture across the organization: define reference architectures, design patterns, and governance; architect data pipelines, feature stores, deployment, monitoring, and MLOps; align with security, infrastructure, and business domains; drive adoption of modern AI capabilities (LLMs, generative AI, vector DBs); mentor teams and communicate technical strategy to stakeholders.

Design and implement high-performance APIs and microservices using Python and AWS, focusing on generative AI and scalable applications.

Lead architecture and build cloud-native, resilient enterprise software and Agentic/Generative AI solutions for Claims Adjudication. Design microservices, APIs, CI/CD and observability; integrate LLMs, vector stores, and orchestration frameworks; ensure AI governance and production reliability while mentoring teams and aligning technical strategy with business outcomes.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Design, develop, and deploy scalable machine learning models and data pipelines for production. Collaborate with data scientists, engineers, and stakeholders, optimize model performance and scalability, automate deployments, monitor production systems, and evaluate emerging ML and cloud technologies to drive business outcomes.

Deploy, maintain, and improve production ML workflows and platforms for media measurement and customer analytics on AWS. Build SageMaker jobs and endpoints, orchestrate pipelines with Step Functions, Lambda, and Airflow, manage model artifacts and CI/CD, troubleshoot production issues, and collaborate on platform enhancements and operational best practices.

Design, develop, test, and support scalable AI-enabled applications and services. Implement LLM, Generative AI, and RAG capabilities, build RESTful APIs and microservices, automate CI/CD and testing, perform code reviews, troubleshoot production issues, and collaborate with product and architecture teams in Agile delivery.