Vetted Feature Engineering Professionals

“ML/AI engineer with production experience across retail and healthcare: built a real-time computer-vision shelf monitoring system at Walmart and optimized edge inference latency by ~30% using TensorRT/ONNX and pruning. Also partnered with CVS Health clinical/pharmacy teams to deliver a medication-adherence predictive model, using Streamlit explainability dashboards and achieving an 18% adherence improvement.”

“AI/software engineer with experience at Visa building a real-time transaction fraud/risk scoring microservice in the card authorization path (Python, Kafka, Kubernetes on AWS) with strict 120–150ms latency constraints and reason-code outputs for downstream decisioning. Owns ML backend end-to-end (data/feature engineering, model training, deployment) and has demonstrated production reliability work including latency spike mitigation, SLO-based observability, drift monitoring, and safe fallbacks to rule-based decisions.”

“Siemens AI engineer who shipped production multi-agent LLM systems across cybersecurity and sustainability, including a vulnerability automation agent that cut manual work 70%. Deep in orchestration (LangGraph supervisor-worker state machines), reliability engineering (async fault tolerance, retries, spike handling), and rigorous evaluation (offline benchmarks, LLM-as-a-Judge improving label agreement 28.9%) with measurable production guardrails.”

“AI/ML engineer who has shipped production AI systems end-to-end, including an automated multi-channel (Gmail/WhatsApp/voice) candidate interviewing workflow and an enterprise RAG knowledge search platform. Demonstrates strong production rigor (monitoring, A/B tests, guardrails, schema validation, shadow testing) with quantified impact: ~60–70% reduction in interview evaluation time and ~20–30% relevance gains in RAG retrieval.”

“GenAI/LLM engineer who architected and deployed a production RAG “research assistant” for JPMorgan Chase’s regulatory compliance team, focused on safety-critical behavior (mandatory citations, refusal when evidence is missing). Deep hands-on experience with LlamaIndex, Pinecone, Hugging Face embeddings, LangGraph agent workflows, and metric-driven evaluation (golden sets, TruLens), including a reported 28% relevancy lift via cross-encoder re-ranking.”

“Unreal Engine UI engineer focused on scalable, production-ready UI architecture (C++/Slate/UMG/CommonUI) with strong designer enablement via decoupled, interface-driven patterns and MVVM. Demonstrated measurable performance wins: replaced 200+ per-frame Blueprint bindings to cut UI prepass/paint from 4.2ms to 0.5ms and reduced VRAM by ~120MB using texture streaming proxies.”

“Healthcare GenAI engineer who built a HIPAA-compliant, auditable RAG-based claims decision support system at Molina Healthcare, processing 3M claims and delivering major impact (48% faster manual reviews, 43% higher decision accuracy). Deep hands-on experience with LangChain orchestration, vector search (ChromaDB/FAISS), embedding fine-tuning, and safety controls (confidence scoring, rule validation, human-in-the-loop escalation) for clinical workflows.”

“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”

“Built and deployed an agentic RAG assistant in production to automate enterprise knowledge search and multi-step workflows with tool calling, tackling real-world issues like hallucinations, retrieval accuracy, and latency. Demonstrates strong LLMOps and orchestration depth (MLflow, Airflow, LangGraph/LangChain/LlamaIndex) plus a metrics-driven approach to agent testing/evaluation and cross-functional delivery with business stakeholders.”

“ML/NLP engineer with experience at Flatiron Health building a production NLP platform that processed millions of clinical notes, using BERT/BiLSTM-CRF and spaCy to extract and normalize entities from noisy EMR text with oncologist-in-the-loop validation. Also built scalable retail ML workflows (Spark + Kubernetes + feature store caching) and applied vector databases plus contrastive-learning fine-tuning to improve retrieval relevance and recommendations.”

“AI/ML engineer at J.P. Morgan Chase who deployed a production financial-risk prediction platform combining CNN/LSTM/gradient boosting on AWS SageMaker, with automated drift-triggered retraining and governance-grade fairness testing. Leveraged SageMaker Clarify plus SMOTE and LLM-generated synthetic data to improve minority-group F1 by 0.12, and communicated results to non-technical risk/ops teams via Power BI dashboards.”

“ML Engineer with recent Goldman Sachs experience building and deploying a production RAG/LLM assistant for summarization, drafting, and internal knowledge retrieval across financial, risk, and compliance documents. Designed for heavy regulatory constraints and scaled to 10,000+ concurrent users using Kubernetes-based orchestration, dynamic LLM routing, and rigorous testing (adversarial prompts, A/B tests, load simulations) with privacy controls like differential privacy.”

“Completed an Amazon externship building a GPT-4 + RAG pipeline to summarize themes from hundreds of employee reviews for workforce analytics aimed at improving warehouse retention. Emphasizes production-readiness through labeled-data evaluation, source attribution for explainability, human-in-the-loop review, and rigorous data cleaning/observability to debug real-world LLM workflow issues.”

“Full-stack engineer with fintech/trading domain experience (Fidelity) and startup SaaS CRM/billing platform work (Zoho), building real-time portfolio analytics and trade-processing systems. Strong in microservices, event-driven architectures (Kafka/WebSockets), and AWS/Kubernetes operations with measurable performance gains (~34–35% latency reduction) and maintainability improvements (~40% faster deployments). Targeting a founding full-stack engineer role in NYC with meaningful equity.”

“Built and productionized an internal LLM-powered document Q&A system at Morgan Stanley using a LangChain-based RAG pipeline (FAISS + OpenAI) with AWS ingestion (S3/Lambda), handling 100k+ pages and cutting lookup time ~35% while keeping responses under 3 seconds. Strong on reliability: automated evals/CI (pytest + GitHub Actions), CloudWatch monitoring, drift detection (prompt drift and fraud-model drift), and security controls (IAM + app-level authorization) in a financial-services environment.”

“Backend/full-stack engineer who has owned production systems end-to-end, including a Dockerized Node.js/TypeScript probabilistic fault-tree analysis service for nuclear safety research deployed on AWS. Also built and operated a FastAPI-based RAG pipeline over 200+ PDFs using FAISS, focusing on low-latency, idempotent workflows and strong observability; experienced with API design and Playwright E2E automation across React/Angular projects.”

“GenAI/LLMOps practitioner who deployed a production RAG-based customer service and knowledge retrieval system for a global bank using LangChain, FAISS/Azure Cognitive Search, GPT-4/Claude, and Guardrails—driving a reported 35% Q&A accuracy lift while reducing handle time and escalations. Also partnered with non-technical leaders at CVS Health to deliver ML-driven supply chain risk and inventory insights via anomaly detection, NLG summaries, and stakeholder-friendly dashboards.”

“Founder of ANTARI Studio who builds metrics-driven mobile game concepts and validates them through playable prototypes, publisher feedback, and real-market launches with platform partners. Has experience taking a niche casual puzzle opportunity from benchmarking and lean prototyping into production and launch, using engagement/retention/monetization KPIs to guide iteration and scaling decisions.”

“Full-stack engineer focused on high-throughput document/financial data platforms, building Angular/React front ends and Spring Boot microservices with Python/Flask services for heavy processing. Experienced in designing non-blocking, asynchronous workflows (Celery/RabbitMQ) and deploying containerized systems to AWS ECS with auto-scaling and CloudWatch monitoring.”