Vetted Machine Learning Professionals

“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.”

“Backend Python engineer who owned an AI-driven healthcare staffing matching service, rebuilding the model inference/data pipeline to eliminate blocking bottlenecks and cutting API latency by ~33%. Experienced running Python services on Kubernetes with GitOps/ArgoCD, and has executed a cloud-to-on-prem rollout under tight resource and tooling constraints while also building event-driven streaming updates via a message broker.”

“Built a production internal LLM/RAG assistant at CVS Health to cut time spent searching long policy and clinical guideline PDFs, combining fine-tuned BERT/GPT models with FAISS retrieval and a FastAPI service on AWS. Demonstrates strong real-world reliability work (document cleanup, hallucination controls, monitoring/drift tracking with MLflow) and close collaboration with non-technical clinical operations teams via demos and feedback-driven iteration.”

“Customer-facing software engineer who builds configurable, extensible solutions under vague requirements (e.g., a bin-packing-style resource allocation feature) and iterates via frequent sandbox deployments and customer testing. Has hands-on experience integrating multiple shipping APIs, navigating outdated/unclear documentation, and resolving production issues quickly through log-driven debugging and safe rollback procedures.”

“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/AI engineer with defense and commercial analytics experience: deployed a real-time aerial object detection system at Dynetics (YOLOv5 + TorchServe in Docker on AWS EC2) with drift-triggered retraining and 99.5% uptime, tackling ambiguous targets and weather degradation. Previously at Fractal Analytics, built and explained a churn prediction model for marketing stakeholders using SHAP and delivered it via a Flask API into dashboards, driving a reported 22% attrition reduction.”

“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.”

“Data/ML practitioner with experience at UnitedHealth Group building a fraud claims detection solution combining structured claims data and unstructured notes, validated with compliance stakeholders to improve actionable accuracy. Also applied embeddings, vector databases, and fine-tuned language models in a Bank of America capstone to detect threats/anomalies in financial documents, with production-minded Python ETL workflows using Airflow.”

“NLP/ML practitioner who built an explainable, clinician-aligned system to detect cognitive decline (Alzheimer’s/stroke-related) from audio responses, achieving 97% accuracy on only a few hundred data points. Also has experience with healthcare claims entity resolution and prototyped a word2vec-based patent search vector database in Elasticsearch, with strong emphasis on testing, interpretability, and scalable Python data workflows.”

“Backend-focused engineer who has built and refactored FastAPI services backed by MongoDB, emphasizing async concurrency, stateless design for horizontal scaling, and performance tuning via indexing and request-level timing. Has implemented production authentication patterns (JWT, SSO, OAuth2 + PKCE) and user/org-scoped access controls, and improved reliability of LLM document-extraction APIs with fallback mechanisms.”

“ML/NLP practitioner with banking experience (M&T Bank) who has built a GPT-4 RAG system using LangChain and Pinecone to connect unstructured customer data with internal knowledge bases, improving accuracy and reducing manual lookup time by 50%+. Strong in entity resolution and productionizing scalable Python data workflows, including major performance wins by migrating bottleneck joins from Pandas to Dask.”

“Infrastructure/operations engineer with hands-on production IBM Power/AIX (AIX 7.x, VIOS, HMC) and PowerHA/HACMP clustering experience, including DLPAR changes, failover testing, and incident recovery. Also delivers modern cloud DevOps work—GitHub Actions CI/CD for Docker-to-Kubernetes on AWS and Terraform-based provisioning of core AWS infrastructure (VPC/EKS/RDS/IAM) with controlled rollouts and drift checks.”

“Full-stack product engineer who has shipped polished customer-facing experiences across iOS (SwiftUI), web (Next.js/React/TypeScript), and Python backends. Built systems ranging from an escalating smart-reminder engine to a sub-200ms search UI over 6M+ court records, and owned AWS production operations including resolving a real DB-connection-exhaustion incident with scaling and architectural hardening.”

“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.”

“Data engineer with Charter Communications experience modernizing large-scale AWS data lake pipelines: ingesting S3 data, validating against legacy systems, transforming with PySpark/Spark SQL, and serving via Iceberg/Delta tables. Worked at 50M–300M record scale, delivered >99.5% data match, and built monitoring/alerting (CloudWatch/SNS) plus retry orchestration (Step Functions) and data quality gates (Great Expectations).”

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

“Full-stack engineer with hands-on ownership of real-time, Kafka-driven systems in production, spanning React/TypeScript frontends, Spring Boot/Node backends, and AWS (EKS/ECS/EC2) operations. Notable for modernizing legacy batch workflows into event-driven architectures with measurable impact (35% faster risk calculations, 30% better accuracy) and scaling to 2x volume using reliability patterns like idempotency, retries, and staged rollouts.”

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.”

Husayn El Sharif

Screened

Senior Data Scientist specializing in geospatial ML and environmental analytics

Atlanta, GA16y exp

“Applied ML practitioner who deployed a near-real-time water-quality monitoring tool for Gwinnett County by fusing ESA satellite imagery with in-situ measurements to predict chlorophyll-A and support early warnings for harmful algal blooms. Also working on a multimodal deep-learning project combining skin lesion images with patient tabular/text data (TensorFlow, embeddings) to predict melanoma risk.”

Arunkumar Gangula

Screened

Senior Full-Stack Software Engineer specializing in distributed systems and cloud microservices

Tempe, Arizona11y exp

“Product-minded full-stack engineer from CouponDunia who owned end-to-end notification and recommendation services at million-user scale. Built internal admin/analytics and operations dashboards in React/TypeScript with typed contracts and scalable Node.js REST APIs, and has deep microservices experience with Kafka/RabbitMQ (idempotency, retries/DLQs, partitioning, consumer tuning, and observability).”

Bhanu Gummadi

Screened

Mid-level Backend Software Engineer specializing in cloud-native microservices and FinTech

Bellevue, WA4y exp

“Backend-focused engineer with Mastercard experience building and operating high-volume transaction-processing microservices. Has owned customer-facing banking services end-to-end and built an internal on-call analytics tool that centralized logs/metrics with real-time filtering to speed root-cause analysis and reduce incident investigation time.”

Maxwell Meyer

Screened

Junior Integration Engineer specializing in healthcare interoperability (HL7 v2/FHIR)

Verona, WI2y exp

“Integration engineer at Epic focused on healthcare interoperability, including complex radiology integrations (orders/results) spanning more than a dozen downstream systems and PACS vendors. Experienced coordinating clinical Radiant teams, interface analysts, and networking teams to scope workflows, route/transform messages via an interface engine, and troubleshoot intermittent production issues using structured isolation and live testing.”

Ketan Verma

Screened

Junior Applied AI Engineer specializing in data pipelines and ML systems

College Station, TX2y exp

“Built an end-to-end wafer-data anomaly detection and reporting system at Samsung using PySpark, Random Forest models, SQL, and Grafana to help engineers track faults and take corrective action. Also has strong UX prototyping and validation practices in Figma plus hands-on front-end/full-stack experience (HTML/CSS/TypeScript), including a student project recognized as best design out of 25 teams, and early-stage startup experience pivoting a product based on user interviews into a real-time in-context feedback overlay.”

Nisarg Shah

Screened

Junior Machine Learning Engineer specializing in geospatial analytics and computer vision

Tempe, Arizona1y exp

“Built and evolved a geospatial ETL + API platform that processes pixel-wise satellite imagery in PostgreSQL/PostGIS into low-latency farm-level time-series metrics for an interactive dashboard, using precomputed hotspot analysis to reduce latency by 75–80%. Experienced in FastAPI-style API contract design (OpenAPI), caching, server-side filtering/compression, and production-minded security patterns (RBAC, session-derived authorization, password hashing) with disciplined rollback/versioning practices.”