Vetted Random Forest Professionals

“Backend/AI engineer with internship experience building an AI-powered financial insights platform (FastAPI, Redis, BigQuery) and prior HCL experience leading a monolith-to-microservices refactor (Flask, Kafka) using blue-green deployments. Demonstrates strong performance/security focus (OAuth/JWT/RBAC, encryption) and measurable impact on latency, downtime, and ML model reliability; MVP was submitted to Google’s accelerator program.”

“Built and shipped an agentic RAG chatbot module for NexaCLM to answer questions across large volumes of contracts while minimizing hallucinations and incorrect legal interpretations. Implemented routing between vector retrieval and ReAct-style agent retrieval plus an automated grading/validation layer (cosine-similarity thresholds, retries) and deployed via GitHub Actions to Azure Container Apps, partnering closely with legal stakeholders to define risk/clause-focused objectives.”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”

“ML/computer-vision practitioner who shipped a CycleGAN-based bilingual handwriting translation demo (English↔Telugu) for low-resource scripts using unpaired datasets, focusing on preserving handwriting style and real-time deployment via Gradio. Also delivered a medical imaging pipeline by fine-tuning ResNet-50 and ViT-B/16 for pneumonia detection, emphasizing reproducibility, measurable evaluation, and stakeholder-friendly iteration.”

“Built a production-style LLM document summarization/generation workflow that mitigates token limits and reduces hallucinations using semantic chunking, FAISS-based embedding retrieval (top-k via cosine similarity), and section-wise generation. Orchestrated the end-to-end pipeline with AWS Step Functions and aligned outputs with sales stakeholders through demos, visuals, and documentation.”

“Applied AI/ML engineer with hands-on production experience building a RAG-based AI assistant for pharmaceutical maintenance troubleshooting using LangChain + FAISS/Pinecone, including a custom normalization layer to handle inconsistent terminology and duplicate document revisions. Also built Airflow-orchestrated pipelines for document ingestion/embeddings and predictive maintenance workflows (SCADA ETL, drift-based retraining), and partnered closely with production supervisors/quality engineers via Power BI dashboards and real-time alerts.”

“LLM/RAG engineer who has built and shipped production assistants, including a RAG-based teaching assistant (Marvel AI) using LangChain/LlamaIndex/ChromaDB with OpenAI embeddings and Redis vector search, achieving ~30% accuracy gains and ~35% latency reduction. Also deployed FastAPI services on Google Cloud Run with observability and prompt-level monitoring, and partnered with non-technical ops stakeholders to deliver an internal policy-document RAG assistant.”

“Cross-functional operator/chief-of-staff style leader who took a product from prototype to a live pilot in 3 months, spanning public-sector data normalization, an ML matching engine, a secure API, and KPI/investor demo instrumentation. Strong focus on executive alignment and productivity via Notion-based operating systems plus automated reporting (Python/Power BI), with experience supporting fundraising and go-to-market narratives.”

“Built and deployed a production LLM-driven document understanding pipeline using LangChain/LangGraph, focusing on reliability via step-by-step prompting, validation checks, and monitoring. Also partnered with non-technical marketing stakeholders at Heartland Community Network to deliver an XGBoost targeting model surfaced in Power BI, improving campaign conversion by 12%.”

“Built and deployed a production AI invention/patent review platform that compares invention submissions against patent rules to provide instant feedback, reportedly cutting legal team review time by ~80%. Learned Snowflake Cortex LLMs and production deployment (Docker + AWS) on the job, and validated system quality through human-in-the-loop testing with experienced legal stakeholders.”

“Backend/ML engineer focused on clinical AI transparency who built ShifaMind, an explainability-enforced clinical ML system using UMLS/MIMIC-IV/PubMed data with RAG, GraphSAGE, and cross-attention. Demonstrated strong production engineering via FastAPI API design and safe migrations (feature flags/shadow inference), plus HIPAA-aligned auth/RLS patterns; also delivered a real-time comet detection system reaching 97.7% accuracy.”

“Built and deployed production NLP/LLM systems including a multilingual (5-language) health misinformation detection pipeline with latency optimization (batching/quantization/caching) and explainability (gradient-based attention visualizations). Experienced orchestrating end-to-end AI workflows with Airflow and Prefect, and partnering with customer support ops to deliver an AI agent for ticket summarization and priority classification with clear, measurable acceptance criteria.”

“At Provenbase, built and shipped a production LLM-powered semantic search and candidate matching platform (RAG with GPT-4/Gemini, multi-agent orchestration, Elasticsearch vector search) to scale sourcing across 10M+ candidate records and 1000+ data sources. Drove sub-second performance, cut LLM spend 30% with routing/caching, and improved recruiting outcomes (+45% sourcing accuracy; +38% visibility of underrepresented talent) through bias-aware ranking and tight collaboration with recruiting stakeholders.”