Vetted SQL Professionals

“Built and deployed an LLM + RAG + memory system on a Furhat social robot, adding continuous face/voice recognition embeddings over WebSockets to enable persistent, natural conversations across sessions. Experienced working around real-world hardware/latency constraints and uses Datadog plus structured debugging/rollback practices for stabilizing customer-facing LLM workflows.”

“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.”

“Backend engineer at Goldman Sachs who built LLM-powered reconciliation/reporting services and high-throughput Kafka pipelines (8M+ events/day). Strong in production-grade Python/FastAPI microservices on Kubernetes with GitOps-style CI/CD, plus experience migrating legacy reporting/settlement services onto an internal Kubernetes platform using shadow deployments and gradual cutovers.”

“PhD-trained engineer with ~3.5 years of consulting experience building simulation/ML-driven manufacturing software. Deployed an ML surrogate model as a .NET C# DLL integrated with MES workflows, and resolved a critical pre-production latency issue by redesigning serialization/storage. Also built Python-based integrations across CAD/CAE tools and cloud material databases using an XML data model, with a strong interest in digital twins and real-to-sim/sim-to-real robotics workflows.”

“AI/ML engineer with experience at Accenture and Morgan Stanley, building production LLM systems (GPT-3 summarization) and finance-focused ML models (credit risk and trading anomaly detection). Combines MLOps depth (Docker/Kubernetes, AWS SageMaker/Glue/Lambda, MLflow, A/B testing, drift monitoring) with practical domain adaptation techniques like few-shot prompting and RAG/knowledge-base integration.”

“Full-stack engineer with 3+ years using Java/Spring Boot (Citi) and React, who built a production observability dashboard monitoring 53 microservices across 17 clusters with real-time health/latency tracing and significant performance improvements (cut load time from ~10s). Also designed a serverless AWS face-recognition system (Lambda/S3/SQS) built to handle burst traffic (~1000 concurrent requests), demonstrating strength in scalable, event-driven architectures.”

“AI/ML engineer with production LLM experience in regulated financial services (J.P. Morgan Chase), building a customer response engine to automate first-contact resolution while addressing privacy, bias, compliance, and scale. Strong MLOps/orchestration background (Airflow, Docker/Kubernetes, AWS Step Functions, Azure ML/SageMaker) plus proven ability to integrate with legacy systems and drive stakeholder adoption through dashboards, auditability, and training.”

“Engineering leader/CTO-type with deep experience building and scaling a vehicle routing platform at Transdev On Demand, including a nationwide rollout to 22 US airports ahead of schedule. Drove engineering best practices (CI/CD, high test coverage, pair programming, automated deployments) and led a multi-tenant architectural upgrade to expand the routing engine to additional business lines and external customers.”

“Backend-focused engineer who built a Python/Flask task-management API with JWT/RBAC, modular service/repository architecture, and PostgreSQL/SQLAlchemy performance optimizations (indexes, lazy loading, bulk ops, pooling). Also implemented multi-tenant data isolation strategies and built an OpenAI-powered document summarization workflow using chunking, async processing, Redis background workers, and caching to improve throughput.”

“GenAI Engineer at Infosys who built and deployed a production multi-agent RAG system for a top-tier bank, scaling to ~50,000 queries/day with 99.9% uptime. Drove measurable gains (45% accuracy improvement, 30% API cost reduction) through open-source LLM fine-tuning, Pinecone indexing/retrieval optimization, and AWS-based MLOps/monitoring, and has experience enabling adoption via developer workshops and customer-facing collaboration.”

“Researcher from the University of South Dakota who built a production medical RAG system to help interpret model predictions by retrieving relevant clinical notes and medical literature, overcoming retrieval accuracy and imaging-dataset challenges through semantic chunking and metadata-driven indexing. Also has hands-on orchestration experience with Airflow and Azure Data Factory, plus a pragmatic approach to LLM evaluation and stakeholder-driven iteration.”

“Applied ML to physical EV fleet systems at ST Labs, building a real-time CNN-LSTM fault prediction pipeline from streaming vehicle telemetry and addressing live data alignment issues via resampling/interpolation and buffered inference. Also developed a V2G/G2V energy transfer algorithm to automate charging/discharging for profit optimization, and made high-impact low-latency pipeline decisions at Astera Holdings using profiling, replay testing, and live A/B validation.”

“Healthcare/clinical ML practitioner who built and productionized ClinicalBERT-based pipelines to extract and standardize oncology EHR data, improving downstream model F1 from 0.81 to 0.92 while controlling training cost via LoRA/QLoRA. Experienced orchestrating real-time AWS ETL/ML workflows (Glue, Lambda, SageMaker) and partnering with clinicians using SHAP-based interpretability, contributing to an 18% reduction in readmissions and full adoption.”

“Software engineer focused on real-time, low-latency AI pipelines: built an end-to-end mobile-to-backend image classification system using React Native/Expo, Node.js, gRPC, MySQL, and Google Vision AI, optimizing throughput and latency. Also integrated an AI model into a real-time field workflow at DTE via Node.js + Azure Databricks, adding data cleaning/validation and safe fallback logic for reliability in operations.”

“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.”

“Backend engineer who built and owned an AI-powered SMS survey platform for a nonprofit serving at-risk communities (internet-limited users), using Cloudflare Workers + Twilio and a state-machine survey engine. Scaled it to ~10k active users with near-zero downtime, added English/Spanish support, and iteratively improved LLM behavior (Claude 3.7 Sonnet) to handle nuanced, real-world SMS responses reliably.”

“Data/ML practitioner with experience applying NLP and classical ML to large-scale customer data (2B+ records) for segmentation, prediction, and survey-text classification, delivering measurable business impact (~18% engagement efficiency). Has hands-on entity resolution across multi-source datasets and has built embedding-based semantic search using SentenceBERT + a vector database with domain fine-tuning (~20% relevance improvement), plus production workflow experience with Spark/Airflow and cloud tooling (AWS/Azure).”

“AWS Solutions Architect intern who advised customers on securing a multi-tenant LLM-based SaaS, including isolation strategy tradeoffs and production guardrails against prompt injection. Has experience investigating a prompt-injection incident using logs/traces and TTP-style documentation, and designing scalable SDK/agent integrations via asynchronous worker architecture with prompt versioning.”

“Full-stack engineer with end-to-end ownership experience, including building a real-time campaign/inventory dashboard at P&G using React/TypeScript, Spring Boot, GraphQL/REST, Redis, Docker, and AWS (EC2/RDS/S3) with Prometheus/Grafana observability. Demonstrates strong performance and reliability focus (p95 tuning, caching, idempotent event-driven ingestion with DLQs/reconciliation) and has shipped MVPs in ambiguous early-stage environments.”

“Operations and general management leader with experience spanning Citibank (Regional VP), a startup COO/GM role, and industrial operations at Tatco. Has delivered high-impact operational and technology changes including a Paychex payroll migration for 150+ employees and an ERP/subscriber migration of 200,000+ records that improved marketing execution and sales enablement, while also managing executive alignment and sensitive workforce matters.”

“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.”

“Built and owned "Cortex," an AI agent that helps users understand large GitHub repositories by mapping architecture and relationships between files/folders in minutes. Implemented an agentic, multi-stage prompt decomposition approach and validated it across open-source repos, while also doing legacy service modernization work involving dependency upgrades and refactors.”