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Vetted Random Forest Professionals
Pre-screened and vetted.
Senior Data Scientist specializing in GenAI, MLOps, and cloud data platforms
Senior AI/Full-Stack Engineer specializing in Generative AI and LLM platform integration
Mid-level Data Scientist / AI/ML Engineer specializing in financial services and GenAI
Mid-level AI/ML Engineer specializing in NLP, LLMs, and fraud/AML analytics
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Data Scientist specializing in ML, NLP, and scalable data pipelines
Mid-level Data Scientist specializing in pricing, revenue optimization, and operational efficiency
Mid-level Data Scientist specializing in NLP, risk analytics, and MLOps
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Mid-level Data Scientist specializing in GenAI, NLP, and cloud MLOps
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Mid-level Data Scientist specializing in ML, NLP, and forecasting across finance and retail
Mid-level Data Engineer specializing in cloud data platforms and BI analytics
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”
Nakul Reddy Sarasani
Junior Full-Stack Software Engineer specializing in cloud-native distributed systems
“Software engineer with JPMorgan Chase experience building a real-time operations console backend on Spring Boot/Kafka/Kubernetes and resolving peak-load latency through profiling, indexing, caching, and async processing. Also built and owned an AI-driven digital-archives metadata pipeline during a master’s at UNT using OCR + LLaMA-based prompting with validation, near-human accuracy, and human-in-the-loop guardrails.”
Laxminarayana Yaga
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”