Vetted Logging Professionals

“Backend/data platform engineer who recently owned an end-to-end large-scale financial data platform delivering real-time decision support for finance and operations. Has hands-on experience modernizing legacy batch pipelines into AWS cloud-native ELT with parallel-run cutovers, strong data quality controls (dbt-style tests, reconciliation), and measurable improvements in runtime, cost, and SLA compliance. Also builds scalable, secure FastAPI microservices using Docker, ALB-based horizontal scaling, Redis caching, and managed auth with Cognito/Supabase plus Postgres RLS.”

“Capgemini engineer who helped take a travel insurance platform from prototype demos to a stable production system by clarifying requirements, hardening API contracts, and adding validation/logging to handle real customer data and external integrations. Experienced in real-time troubleshooting of complex workflows (including LLM/agentic-style workflows) through strong observability practices, and in leading practical developer-focused demos that accelerate client integration and adoption.”

“Built an AI journaling app at HackCU 2025 featuring a speaking AI avatar with long-term memory via RAG (ChromaDB) and low-latency microservices coordinated through Kafka, including deployment under AMD/non-CUDA constraints using a quantized Llama 8B model. Also has Goldman Sachs experience deploying a Trade UI on Kubernetes with CI/CD rollback automation, plus a healthcare AI internship at CU Anschutz collaborating closely with physicians on diagnostic reasoning and dataset annotation.”

“AI/LLM engineer with production experience at Charles Schwab building a RAG-based assistant to help 5,000+ reps answer complex financial policy questions. Implemented a multi-layer anti-hallucination approach (GNN-driven ontology/graph retrieval + citation-only answers) and compliance-focused guardrails (Azure AI Content Safety) in partnership with audit/compliance stakeholders.”

“AI/ML engineer currently at BlackRock who deployed an AI-powered sentiment analysis microservice into a task management platform to prioritize and escalate high-risk/frustrated tickets from free-text comments. Experienced running production microservices on AWS EKS with Docker/Kubernetes/Helm and provisioning infrastructure via Terraform, with strong MLOps rigor (MLflow evaluation pipelines, canary rollouts, and real-time monitoring) and cross-functional collaboration with product/operations.”

“Built and deployed profitprops.io, a sports betting player-props prediction product using ML/AI. Implemented backend APIs with FastAPI/Express.js and Supabase, trained models on AWS GPU (P3) using Docker + RAPIDS, and set up CI/CD with GitHub Actions while working around cost constraints and data-collection hurdles (EC2 proxy rotation/rate limits).”

“ML/AI engineer with Capital One experience building production-grade customer segmentation and fraud detection systems combining NLP (transformers) and anomaly detection. Strong MLOps and orchestration background (PySpark ETL, MLflow, Airflow, Docker/Kubernetes, Azure ML) with real-time monitoring/alerting and performance optimizations like quantization and caching, plus proven ability to deliver business-facing insights through Power BI/Tableau for marketing stakeholders.”

“Full-stack engineer with primary depth in .NET Core and Python who has built and deployed end-to-end AWS applications (Lambda, API Gateway, S3, CloudFront) and supported them in production. Experienced in scaling large, data-driven workloads using queues/background workers, batching, and database tuning, with strong focus on API contracts, observability, and resilience patterns; also has hands-on React/TypeScript and some Spring Boot exposure.”

“McKinsey data/ML practitioner who led production deployment of an entity resolution + semantic search platform for unstructured finance and healthcare data, integrating with legacy systems under HIPAA constraints. Deep hands-on stack across transformers (spaCy/HF BERT), embeddings + FAISS, and production MLOps/workflow tooling (Airflow, Docker, CI/CD, Prometheus/Grafana), with reported gains of +30% decision speed and +25% search relevance.”

“Product-focused full-stack engineer with FedEx experience building an internal logistics dashboard for near real-time shipment status and performance metrics using Next.js App Router + TypeScript. Strong in production ownership and performance work—uses React Profiler/Chrome DevTools to eliminate expensive re-renders and applies Postgres indexing/query tuning validated via EXPLAIN ANALYZE to improve dashboard responsiveness.”

“Infrastructure-focused engineer with Vonage experience modernizing early-stage cloud architecture (Terraform modularization, blue-green deployments, containerization, and zero-downtime database migration planning to Aurora). Also built a local end-to-end side project, Vastu AI, combining a custom-trained YOLO model (Roboflow-labeled data) with a locally hosted LLM via Ollama to generate a vastu compliance report from floor-plan images.”

“Data engineer/platform builder with experience across PwC and Liberty Mutual delivering high-volume, production-grade pipelines and real-time data services. Has owned end-to-end streaming + batch architectures on AWS and Azure, including web scraping systems, with quantified reliability gains (99.9% availability, 90%+ error reduction, 30% latency reduction) and strong observability/CI-CD practices.”

“Built and productionized a Python/LLM-based pipeline at Catalyst Solutions to automate healthcare RFP processing, turning unstructured documents into validated JSON/Excel with schema validation, confidence scoring, and human-review routing. Delivered major operational impact (hours-to-minutes processing, ~60% efficiency gain; 50+ RFPs processed) and modernized legacy scripts into a staged, more reliable architecture using incremental refactoring and fallback comparisons.”

“Software Engineer at Armstrong Robotics building multithreaded C++ perception/planning/control software for robotic arms running commercial dishwashers deployed across multiple restaurant sites (up to ~2,000 dishes/day per installation). Strong in production operations: on-call debugging with deep logging/video analysis, rapid hotfixes, Datadog-based monitoring, and a Three.js calibration tool plus large regression test suite to de-risk live deployments.”

“Backend/data engineer focused on AWS-based Python services and data pipelines: built a Django/DRF user management/auth platform deployed with serverless AWS (Lambda/API Gateway) and event-driven workflows (Step Functions/EventBridge), with CloudFormation + Jenkins for automated delivery and Secrets Manager/Parameter Store for secure config. Also delivered AWS Glue ETL from S3 to RDS with schema evolution controls and incident-driven improvements, and has demonstrated measurable SQL tuning impact (minutes-to-seconds).”

“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.”

“Backend engineer focused on building production-ready Python services (Flask/FastAPI) with strong performance and scalability instincts—Celery/Redis background processing, robust multi-tenant isolation (Postgres RLS), and pragmatic CI/Docker operations. Demonstrated measurable DB optimization impact (cut a critical analytics query from ~1–2s to ~100–150ms) and has hands-on experience integrating LLM/ML workflows (OpenAI, LangChain, embeddings, Redis/FAISS vector stores) without degrading API responsiveness.”

“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.”

“Backend/automation engineer who led the design of an OOP Python test automation framework for AWS infrastructure (Behave + Jenkins), cutting regression effort from weeks to a 3–4 hour run. Has hands-on cloud and DevOps experience across AWS (boto3, ECS, AMI automation via GitHub Actions) plus data/migration work including on-prem-to-cloud Oracle Retail DB migration with rollback planning and a Kafka + ML fraud-detection streaming pipeline.”

“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 operated a production TypeScript backend for a stateful conversational quoting chatbot at Tringapps, orchestrating multi-step workflows and session state while integrating with Salesforce and NetSuite. Implemented validation/retry logic, modular architecture, and strong logging/observability to handle real-world edge cases and external API failures.”

“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.”

“Backend-focused engineer with strong .NET/Angular experience building enterprise financial and healthcare systems, including microservice APIs deployed with Docker/Kubernetes and AWS ECS. Demonstrates production reliability skills across secrets management (Secrets Manager/IAM), incident response (CloudWatch + Kafka failover), and data engineering patterns from SSIS ETL (data quality, incremental recovery), plus proven SQL tuning with a 10-minute report reduced to under 30 seconds.”