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Vetted Machine Learning Engineers in Missouri
Pre-screened and vetted in Missouri.
Mid-level AI/ML Developer specializing in FinTech fraud detection and GenAI assistants
Mid-level Generative AI Developer specializing in LLM agents and RAG systems
Mid-level Generative AI Engineer specializing in RAG, LLMOps, and enterprise decision intelligence
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.”
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Mid-level Data Scientist / AI/ML Engineer specializing in Generative AI and healthcare analytics
Anvith Reddy T
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”
Karthik Gantasala
Mid-level Generative AI Engineer specializing in LLM agents and RAG
“GenAI/LLM engineer who built and deployed a production RAG system for enterprise document search and decision support, cutting manual lookup time by 40%+. Experienced with LangChain/LangGraph agent orchestration plus Airflow/Prefect for ingestion and incremental reindexing, with a strong focus on reliability (testing, observability) and stakeholder-driven metrics.”
Varun Kothapalli
Mid-level AI/Machine Learning Engineer specializing in Generative AI, NLP, and MLOps
“Built a production LLM/RAG document analysis system for large financial documents (credit reports/PDFs) to help business analysts extract insights faster. Implemented end-to-end pipeline orchestration with LangChain, vector search (e.g., FAISS), and hallucination controls (context grounding, similarity thresholds, and no-answer fallback), delivered as a Dockerized Python API.”
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in Financial Services, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in NLP, LLMs, and production ML pipelines
Mid-level Machine Learning Engineer specializing in NLP, computer vision, and LLMs
Mid-level Data Scientist / AI-ML Engineer specializing in MLOps, geospatial analytics, and GenAI
Mid-level Data Scientist / AI/ML Engineer specializing in MLOps, geospatial analytics, and GenAI
Karthikeya Arra
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”
Mid-level Machine Learning Engineer specializing in MLOps, NLP, and Computer Vision