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Vetted AI & Machine Learning Professionals in Texas
Pre-screened and vetted in Texas.
Popular Titles
Mid-level AI/ML Engineer specializing in Generative AI and cloud-based ML systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and full-stack development
Senior GenAI Engineer specializing in enterprise LLM systems and RAG platforms
Mid-level Data Scientist / ML Engineer specializing in risk, fraud, NLP and recommender systems
Mid-level Full-Stack GenAI/ML Engineer specializing in agentic AI and RAG systems
Mid-level AI/ML Engineer specializing in NLP, LLMs, and fraud/AML analytics
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Entry-level Machine Learning Engineer specializing in LLM systems and AI infrastructure
Mid-level AI Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level Applied AI Engineer specializing in Generative AI and RAG systems
Siva Pothuru
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and cloud-native ML
“LLM/agent engineer at USAA who built a production GPT-4o RAG conversational assistant for financial analysts, focused on regulatory interpretation and internal documentation search. Emphasizes compliance-grade reliability with strict grounding, safe fallbacks, and full auditability via MLflow/DVC plus human-in-the-loop review; reports ~45% reduction in ticket resolution time.”
Mid-level Machine Learning Engineer specializing in MLOps and production ML systems
Mid-level Data Scientist / Machine Learning Engineer specializing in NLP and computer vision
Mid-level Full-Stack AI Engineer specializing in agentic LLM platforms
Mid-level Machine Learning Engineer specializing in MLOps and applied data science
PremKumar Gandla
Mid-level AI/ML Engineer specializing in MLOps, NLP, and scalable model deployment
“Built and deployed a production autonomous AI data analyst agent (LangChain + GPT + Streamlit on AWS) that turns natural-language questions into validated SQL, visualizations, and insights, cutting manual analysis time by ~50%. Emphasizes reliability and MLOps: schema-aware validation/guardrails to prevent hallucinations, scalable large-data processing, and Azure DevOps CI/CD + MLflow for automated deployment and experiment tracking.”
Snehitha Penumaka
Mid-level AI/ML Engineer specializing in predictive modeling and cloud ML pipelines
“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”
Aravind Gudipudi
Mid-level AI/ML Engineer specializing in MLOps and cloud-deployed ML systems
“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”
SaiGanesh Konagalla
Mid-level ML Engineer specializing in NLP and Generative AI
“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”
Manish Vemula
Mid-level Machine Learning Engineer specializing in real-time pipelines and NLP/GenAI
“ML/MLOps practitioner from Discover Financial who built and deployed a real-time AI fraud detection platform (LSTM + VAE) on AWS SageMaker with Docker/FastAPI and Jenkins-driven CI/CD. Demonstrated measurable impact (30% accuracy lift, 25% fewer false alerts) and deep expertise in class-imbalance mitigation, drift monitoring, and orchestration (Airflow/Kubeflow), plus strong stakeholder adoption via Power BI dashboards for fraud/compliance teams.”