Vetted Logistic Regression Professionals

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Growth/creative marketer from Esleydunn Games who uses Google Analytics to integrate cross-channel performance data (TikTok, YouTube, LinkedIn, Facebook) and run structured A/B tests on video ad length and layout. Reported reducing CPA by 20 per customer when leveraging YouTube and TikTok, and improved CTR through CTA/button placement testing and ongoing user-feedback loops (forum/WeChat topics).”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

“Analytics-focused candidate with internship and project experience at Recotap and CoUnderscore, combining SQL, Python, and BI dashboards to turn messy marketing and engagement data into decision-ready reporting. Stands out for tying analytics work to business outcomes, including ~15% CTR improvement, identifying ~40% misattributed spend, and enabling a ~$75K budget shift through better targeting.”

“GenAI/ML engineer with hands-on experience building production financial intelligence and document summarization systems at Citibank. Stands out for combining LLM fine-tuning, hybrid RAG, multi-agent workflows, and strong MLOps/observability practices to deliver measurable business impact, including 60% faster analyst retrieval, 31% higher precision, and 99%+ uptime.”

“AI/LLM engineer who built a production intelligent support system using RAG over a vectorized documentation library, addressing real-world issues like lost-in-the-middle context failures and doc freshness via automated GitHub-driven re-embedding pipelines. Emphasizes rigorous agent evaluation (component/E2E/ops) and prefers lightweight, decoupled workflow automation using message brokers (Redis/RabbitMQ) over heavyweight orchestration frameworks.”

“ML practitioner with a QA background who has built end-to-end ML pipelines for a health risk prediction use case (lifestyle + demographics), emphasizing robustness through strict data validation, leakage prevention, and cross-validation. Collaborated with a dietician to sanity-check predictions and refine feature interpretation for real-world practicality; has not yet deployed LLM/AI systems to production and has no hands-on orchestration framework experience but is willing to learn.”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”

“AI/ML engineer with forensic analytics and healthcare claims experience (Optum), building production LLM/RAG systems to surface context-driven fraud patterns from unstructured claim notes and explain risk to investigators. Strong in large-scale retrieval performance tuning, legacy API integration with reliability patterns (SQS, circuit breakers), and MLOps orchestration on Airflow/Kubernetes with rigorous testing, monitoring, and stakeholder-friendly interpretability.”

“Full-stack engineer with enterprise experience at Metasystems Inc. (and Qualcomm) building high-traffic, security-sensitive systems—owned a secure transaction processing module end-to-end using Java/Spring Boot, Python/Django, and React. Strong AWS production operations (EKS/ECS/Lambda/RDS/DynamoDB) with IaC (Terraform/CloudFormation), observability, and reliability patterns; also delivered resilient ETL/integration pipelines with idempotency/retries/backfills and achieved a 50% deployment-time reduction through CI/CD and modular refactoring.”

“Built a production multimodal LLM-based vendor risk assessment platform that ingests SOC reports and other documents, uses a strict RAG pipeline with grounded evidence (page/paragraph citations), and dramatically reduces analyst review time. Experienced with LangGraph/LangChain/AutoGen for stateful, fault-tolerant agent workflows, and emphasizes reliability (schema validation, guardrails) plus low-latency delivery (~1–2s) through hybrid retrieval, reranking, caching, and model tiering.”

“At CVS Health, the candidate productionized a RAG-based LLM solution in a regulated healthcare setting, emphasizing reliable data pipelines, LoRA fine-tuning, monitoring, safety guardrails, and A/B testing. They have hands-on experience troubleshooting real-time RAG failures (e.g., chunking/embedding issues) and regularly lead developer-focused demos/workshops while translating technical architecture into business value for stakeholders.”

Alex D'Souza

Screened

Junior Machine Learning Researcher specializing in healthcare AI and security

Davis, CA2y exp

“Research-focused AI/ML candidate who built an fMRI-based classifier to predict schizophrenia treatment effectiveness under small-dataset constraints. Demonstrated pragmatic model selection by moving from a complex GNN to graph-summary feature engineering with logistic regression, significantly improving accuracy and AUC; primarily works in Google Colab with script-based workflows.”

Laasya Muktevi

Screened

Intern Machine Learning Engineer specializing in forecasting, NLP, and RAG systems

San Jose, CA5y exp

“Intern who built and deployed a production LLM-powered contract analysis system for finance teams: Azure Document Intelligence for text/table extraction plus Gemini prompting to surface key terms and risks via an async API and simple UI. Emphasizes reliability in production with fallbacks, guardrails against hallucinations, and operational concerns like latency/cost/versioning, delivering summaries in under 30 seconds instead of hours.”

Srikanth Reddy

Screened

Mid-level AI/ML Engineer specializing in GenAI and financial risk & compliance analytics

Plainsboro, NJ7y exp

“Built and deployed a production LLM-powered financial risk and compliance platform to reduce manual trade exception handling and speed up insights from regulatory documents. Implemented a LangChain multi-agent workflow with structured/unstructured data integration (Redshift + vector DB) and emphasized hallucination reduction for regulatory safety using Amazon Bedrock. Strong MLOps/orchestration background across Kubernetes, Airflow, Jenkins, and monitoring/testing with MLflow, Evidently AI, and PyTest.”

Aishwarya Thorat

Screened

Intern Data Scientist specializing in ML engineering and LLM agentic workflows

San Francisco, CA6y exp

“Built an agentic, multi-step LLM system that generates full-stack code for API integrations using LangChain orchestration, Pinecone/SentenceBERT RAG, and a human-in-the-loop feedback loop for iterative code refinement. Also collaborated with non-technical content writers and PMs during a Contentstack internship to deliver a Slack-based AI workflow that generates and brand-checks articles with one-click approvals.”

“Hands-on ML practitioner who built and deployed an end-to-end phishing email classifier (CLI + simple web app), achieving 98% accuracy and reducing manual security triage. Emphasizes production reliability through input validation, graceful failure modes, monitoring/logging, and iterative error analysis, with experience hardening pipelines against messy backend/database data using fallbacks and idempotent processing.”

“Outbound-focused candidate with experience spanning EV charging/business development and e-commerce agency outreach. They combine cold calling, digital marketing, and message testing to generate qualified conversations, including booking local business owners to speak with a CEO about EV charger installation and app-host partnerships.”

“Robotics-focused engineer with hands-on projects ranging from a solo Dobot Magician Lite tic-tac-toe system (computer vision + minimax) to integrating an LLM with a Dobot arm for real-time pick-and-place via structured action outputs and validation. Also brings prior full-time DevOps experience (Docker/Kubernetes and CI/CD) and has used ROS/Gazebo for simulation work, including exploring improvements to crowd-aware navigation using human-trajectory datasets.”

“Backend/ML engineer who built an end-to-end greenwashing detection system for corporate ESG reports: Python preprocessing pipeline, logistic regression + fine-tuned DistilBERT models, and a Dockerized FastAPI inference service optimized for latency. Internship experience maintaining GitLab CI/CD for TypeScript services (Jest/Playwright), improving pipeline stability and test determinism; familiar with Kubernetes/GitOps concepts and AWS CLI/SSO.”

“GenAI/LLM engineer who recently built a production compliance assistant at State Farm for KYC/AML and regulatory teams, using AWS Bedrock + LangChain with Textract/Lambda pipelines to extract fields, tag risk, and summarize long documents. Implemented RAG, strict structured outputs, and human-in-the-loop guardrails, and reports automating ~80% of documentation work while reducing review time by ~40%.”

“ML/NLP practitioner focused on financial-services document intelligence and compliance workflows—built an end-to-end pipeline to classify documents and extract financial entities from loan applications, emails, and statements stored in S3/internal databases. Strong in entity resolution/record linkage and in productionizing pipelines with GitHub Actions CI/CD, testing, data validation, and Docker, plus semantic search using OpenAI embeddings and a vector database.”

“AI/LLM engineer who has shipped production RAG systems (including a 250K-document compliance knowledge tool on AWS) and focuses on reliability via citations, guardrails, and rigorous evaluation (Ragas/Opik/DeepEval). Also built a LangGraph-orchestrated webcrawler agent that cut research paper extraction from hours to minutes, and collaborated with clinical teams to deliver patient volume forecasting with an optimization layer for staffing.”

Fatemeh Taghvaei

Screened

Junior AI/ML Engineer and Instructor specializing in deep learning, computer vision, and NLP

Chicago, IL2y exp

“Computer-vision practitioner and educator who built a real-time license plate recognition system (OpenCV/Python + KNN) optimized to run on a Raspberry Pi with camera integration. Also designs hands-on deep learning coursework, incorporating recent transformer-based vision research (Vision Transformers) into practical labs on real datasets.”