Vetted Machine Learning Professionals

“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”

“Software developer at Indiana University who built a phishing training platform using TypeScript/React, Node, GraphQL, and DynamoDB, including an admin tool to monitor student completion and send automated reminder emails. Also created TechRent, an IU-only electronics rental marketplace concept focused on improving security by restricting access to university students.”

“Built and deployed a production RAG-based enterprise document intelligence platform for financial/compliance/operational documents on AWS (Spark/Glue ingestion, embeddings + vector DB, LangChain orchestration, REST APIs on Docker/Kubernetes). Deep hands-on experience orchestrating multi-step and multi-agent LLM workflows (LangChain, LangGraph, CrewAI) with strong focus on grounding, evaluation, observability, and cost/latency optimization, and has partnered closely with non-technical finance/compliance teams to drive adoption.”

“Graduate Assistant Intern at Friends University who built and deployed a GenAI-driven requirement understanding system that automates extraction and semantic grouping of technical requirements from large unstructured documents. Demonstrates strong LLM engineering rigor (golden datasets, regression testing, post-processing validation) and production-minded delivery using LangChain/LlamaIndex orchestration, FastAPI microservices, Docker, and cloud deployment.”

“Backend/DevOps-leaning engineer managing multiple customer service platforms end-to-end (requirements through deployment). Built an in-house Python monitoring/alerting solution for Salesforce-to-Java contact sync jobs (Snowflake dependencies) that increased uptime ~60%, and helped modernize delivery by moving the team from manual releases to automated Jenkins-based deployments while coordinating an Oracle EBS→Fusion transition with business/data/IT stakeholders.”

“AI software engineer with experience spanning LLM/RAG production systems and regulated fintech infrastructure. Built an end-to-end natural-language-to-SQL analytics assistant (Weaviate + GPT-4 + Supabase) shipped as an API with 92% accuracy and major time savings for non-technical users, and also owned demand-forecasting and CI/CD/containerization improvements for a Bank of America core banking deployment at Infosys.”

“Video editor with years of deep Adobe Premiere Pro/After Effects experience, including sports docuseries work for Apple TV+ where they navigated IOC/Olympic and brand-logo restrictions by masking logos without disrupting motion or storytelling. Also has significant Paramount+/MTV promo and cross-platform (16:9 + social/YouTube) editing experience, with a strong emphasis on narrative tone, pacing, sound design, and mentoring junior editors.”

“Backend engineer with hands-on experience building real-time, low-latency systems: owned the Python backend for a real-time crowd-monitoring product (top 5% at HackHarvard 2025) using OpenCV, GPU YOLO inference (PyTorch), WebRTC, and OAuth. Also has production Kubernetes/GitOps experience (Helm/Kustomize, GitHub Actions, Argo CD), Kafka-based event pipelines, and executed a minimal-downtime on-prem PostgreSQL migration to AWS EC2.”

“Backend-focused engineer with experience owning a production e-commerce platform end-to-end (TypeScript/Node/Express, React, MongoDB, Redis) including RBAC and contract-based API development. Also worked at Infosys on a large healthcare management system built with Spring Boot microservices, using Kafka for messaging/retries, circuit breakers for resilience, and OpenTelemetry/Swagger for observability and API documentation.”

“ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.”

“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.”

“ML/NLP engineer (Beyond Identity) who built production semantic search and entity-resolution systems over internal security documentation, using LDA + BERT embeddings with FAISS/Pinecone to cut search time by 30%. Also scaled a real-time anomaly detection pipeline to millions of events/day with Spark and AWS Lambda, with strong emphasis on measurable validation (Precision@k, MRR, F1, ARI).”

“AI/ML engineer with banking domain experience (M&T Bank) who built a production credit-risk prediction and reporting platform combining ML models (XGBoost/TensorFlow) with a RAG pipeline (LangChain + GPT-4) over compliance documents. Delivered measurable impact (≈20% better risk detection/precision, 50% less manual reporting) and productionized workflows on Vertex AI/Kubeflow with CI/CD and monitoring; also implemented embedding-based semantic search using FAISS/Pinecone.”

“AI/LLM engineer at Humana who built and deployed a HIPAA-aware RAG system for clinical record retrieval, cutting search time dramatically and improving retrieval efficiency by 30%. Experienced with Spark-scale data preprocessing, QLoRA fine-tuning, LangChain orchestration, and MLflow+SageMaker integration, with a strong testing/evaluation discipline (A/B tests, human eval) to hit 95%+ accuracy and production latency targets.”

“Backend engineer who built and scaled a transaction-processing microservice (150K+ records/day) in a microservices ecosystem, debugging peak-load latency/timeouts via CloudWatch/Grafana, Kafka lag analysis, and DB query tuning (indexes, Redis caching, batching). Also shipped an LLM-powered document assistant end-to-end with prompt/response validation plus retries/fallbacks for production reliability.”

“Backend/AI engineer in healthcare/insurance (mentions Cigna) who has shipped production systems spanning high-reliability APIs, async job architectures (Celery), and LLM/RAG features. Built an LLM document assistant with Terraform-managed AWS infra, semantic search retrieval, and strict permissioning/audit logs, and designed an automated prior-authorization workflow with human-in-the-loop escalation and compliance-driven thresholds.”

“Built a production LLM/RAG solution for healthcare operations teams to query large policy and care-guideline repositories in natural language. Improved domain alignment using vector retrieval plus parameter-efficient fine-tuning and prompt optimization, validated through internal user testing and metrics, cutting manual lookup time by ~40%. Also has hands-on experience orchestrating automated ML pipelines with Apache Airflow.”

“Built and deployed LLM-powered analytics and reporting systems, including a RAG-based assistant over Snowflake that let business users ask questions in plain English instead of writing SQL. Experienced orchestrating LLM agents (LangChain) and serverless reporting pipelines (AWS Lambda/S3/RDS), with a strong focus on grounded outputs, monitoring/evaluation, and data quality—used daily by non-technical finance and operations teams at Cigna.”

“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”

“Full-stack engineer with cloud and GenAI experience who has owned production features end-to-end, including a reporting dashboard optimized from 14s to 5s using query/API refactoring and monitored via AWS CloudWatch. Also productionized an OpenAI-powered chatbot using LangChain with prompt design, guardrails, and evaluation via production logs and user feedback, and has led incremental legacy-to-microservices modernization with parallel run to avoid regressions.”

“Senior technologist (25 years experience) who served as chief architect/CTO for a patented software startup that was acquired. Strong at building scalable, robust, technology-agnostic systems and translating technical value into investor-ready narratives (forecasts, roadmaps, documentation). Currently prefers joining an existing founding team as a key technical leader/mentor rather than leading entrepreneurship solo.”

“Built and shipped AI-powered workflow automation at Oracle, including an MCP-based agentic workflow with tool-calling and guardrails, plus Grafana monitoring and Confluence documentation. Also led a Django monolith-to-microservices migration at Chamsmobile using blue-green deployment and load balancer traffic splitting to avoid regressions while modernizing production systems.”

“Serial founder/engineer who has raised capital for two prior startups and is now building a mental awareness venture. Drove rapid early traction for Fashom after an organic YouTube unboxing sparked demand, then scaled using influencer-led growth with no paid ads for ~2 years, achieving ~150% YoY growth and staying profitable through COVID.”

“Software engineer with medical-domain experience at Philips focused on end-to-end systems integration and reliability, including integrating C# xUnit automated test suites with backend services and CI-driven staging deployments. Also built a Python/scikit-learn typing speed analyzer integrating data collection, model training, and a real-time user interface; interested in transferring this systems/debugging rigor into robotics despite no ROS/robotics project experience yet.”