Vetted Feature Engineering Professionals

“Data engineer in financial services (Northern Trust) who has worked across ingestion, transformation, data quality, orchestration, and serving on AWS (S3/Glue/EMR) with Airflow. Highlights include processing ~15M transactions with validation/anomaly detection for regulatory reporting and improving Snowflake query performance by 27% for risk/compliance reporting. Also built a personal real-time streaming service (FastAPI, Kafka, Redis, Cassandra) and uses production reliability patterns like blue-green/atomic swaps and robust retry strategies.”

Drashti Magia

Screened

Junior AI Engineer specializing in LLMs, multimodal ML, and applied machine learning

San Jose, CA3y exp

“Software engineer with a disciplined, production-minded approach to AI-driven development: uses ChatGPT, Claude, GitHub Copilot, and scoped coding agents to accelerate delivery without giving up architectural judgment. Notably applied a multi-agent workflow on ClinicOps Copilot, using agents for planning, Bedrock/RAG scaffolding, and failure testing while personally owning architecture, grounding quality, and end-to-end review.”

“AI-focused engineer with Infosys experience building Azure/.NET chatbot applications and recent hands-on work with FastAPI/LangChain. Built a hackathon multi-agent legal counsel system showcasing agent orchestration, and emphasizes production readiness via Docker, GitHub Actions CI/CD, pytest automation, and adversarial simulations for auditable AI behavior. No direct robotics/ROS experience to date.”

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

“Analytics-focused candidate with hands-on experience in both supply chain data and AI product analytics. They have built SQL and Python pipelines for messy ERP/inventory data as well as high-volume user event data, and have driven experimentation, retention measurement, and dashboarding for AI avatar and voice/image cloning features.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”

“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”

“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”

“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”