Vetted Monitoring Professionals

“Backend/infra engineer with experience across Nestle, J.P. Morgan, and Capgemini, combining ML systems work (YOLOv8/PyTorch object detection with TFLite edge deployment) with production-grade cloud/Kubernetes operations. Has delivered measurable impact via AWS migrations (25% cost reduction, 99.9% availability), microservice modernization (35% faster processing), and low-latency Kafka streaming for financial dashboards (<100ms) using DLQs and idempotent consumers.”

“Built and deployed a RAG-based clinical knowledge assistant at GE Healthcare to help clinicians query large volumes of messy, unstructured clinical documents with grounded, cited answers. Hands-on across the full stack (OCR/ETL, de-identification for PHI, Azure OpenAI embeddings, Cosmos DB indexing, FastAPI/Django) with production monitoring via LangSmith and performance tuning through batching and index optimization.”

“Backend engineer with strong AWS/CI/CD experience (multi-repo deployments, Lambda + core app, immutable ECR and image promotion) and a published master’s thesis building an ML framework for Solar PV energy prediction and CO2 reduction impact modeling using ensemble and meta-learning approaches benchmarked against SAM.”

“Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.”

“Customer-facing technical professional who designs solution architecture and builds PoCs for regulated customers, iterating via biweekly demos and direct feedback to reach production-ready implementations. Regularly delivers technical demos (~2/month for nearly a year) and partners with sales/customer-facing teams by refining technical implementations until they match customer requirements.”

“Platform-focused engineer experienced in productionizing ML/LLM systems: containerized a local prototype, implemented CI/CD, deployed to Kubernetes with scaling controls, and added monitoring/logging. Comfortable diagnosing real-time issues in LLM/agent workflows using logs/metrics and incident stabilization tactics, and supports sales calls by clearly explaining production scalability to unblock customer decisions.”

“Backend/Full-stack engineer who built a Go-based API for a real-time eye-tracking system (calibration/recording/streaming) and debugged intermittent long-session timeouts through improved observability and concurrency refactors. Also shipped an LLM-driven "Doctor Simulator" product end-to-end (React/Node/Go/MongoDB/OpenAI), including structured prompts, deterministic verification/termination logic, and production guardrails like validation, retries, and prompt versioning.”

“Product-focused full-stack engineer who built and shipped subscription/billing features end-to-end (FastAPI + React/TypeScript), including a self-serve cancellation flow with save-offers that reduced support tickets and improved churn insights. Experienced operating on AWS (ECS/EC2, RDS, S3/CloudFront) and handling real production scaling incidents through DB/query optimization, idempotent API design, and strong observability practices.”

“LLM/RAG engineer who has shipped production systems in high-stakes domains (fraud analytics at Mastercard and security compliance as a CI/CD gate). Strong focus on reliability: hybrid retrieval for latency, citation-backed outputs for trust, and code-driven eval/regression pipelines using golden datasets. Also built scalable OCR-based ingestion for messy classroom artifacts (handwriting, PDFs, whiteboard photos) using Go/Python and cloud services.”

“Infrastructure reliability/capacity-focused engineer with hands-on IBM Power/AIX (LPAR/DLPAR, HMC, VIOS) performance troubleshooting and modern cloud-native delivery experience. Built production CI/CD and Terraform-managed AWS/EKS environments, and has led real incident recoveries spanning Kubernetes autoscaling and AWS quota constraints with concrete RCA and prevention improvements.”

“Backend-leaning full-stack engineer with Walmart experience building and operating high-volume media upload and processing systems. Strong in Java/Spring Boot, Postgres performance tuning (EXPLAIN/ANALYZE), and durable workflows using Kafka/Spring Batch with retries and idempotency, plus production ownership via monitoring and optimization; familiar with Next.js/TypeScript and modern React performance patterns.”

“Full-stack engineer with production ownership across React/TypeScript, Node/Express, and Postgres, including zero-downtime releases and rollbackable migrations. Demonstrated measurable performance wins (20% response-time reduction) through DB query profiling and batching, plus hands-on AWS operations (ECS/Lambda/CloudWatch) and reliability patterns for ETL (retries, DLQs, idempotency). Experience shipping microservices quickly in ambiguous, fast-paced environments (Deloitte).”

“Backend/platform engineer who has built and run production Python/Flask + Kafka microservices processing RFID and camera/RFID fusion streams for near-real-time retail cart updates at ~4–5M events/day. Strong in reliability/performance debugging (p99 latency, Kafka lag, Cosmos DB RU hot partitions) with measurable impact including ~30% database cost reduction, and has also shipped an end-to-end vulnerability scanning workflow with DynamoDB-backed state, idempotency, and robust retry/verification guardrails.”

“Backend/platform engineer who built a high-scale audience segmentation and real-time targeting system using Spark/Glue + S3/Hudi and low-latency API services backed by Redis/relational stores. Demonstrates strong production rigor: Spark performance tuning to eliminate OOM failures, API idempotency/caching to cut p95 latency ~40%, and careful dual-run/feature-flag migrations with reconciliation and rollback runbooks. Experienced implementing layered security with JWT/OAuth, RBAC/ABAC, and database row-level security to prevent privilege escalation.”

“Built and deployed a production LLM-driven Dungeons & Dragons game where the model acts as a dungeon master, adding a structured combat system and a macro-state tree to ensure campaigns converge to a clear ending. Fine-tuned Gemini 2.5 Flash on Vertex AI and deployed on GCP with Kubernetes, using RAG over DnD rules/spells plus multi-agent orchestration (intent-based routing between narrative and combat agents) to reduce hallucinations and improve reliability.”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“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.”

“Capital One engineer focused on fraud and payments platforms, owning end-to-end services and internal tools used by fraud analysts. Built high-traffic Kafka/REST systems and real-time React/TypeScript dashboards (WebSockets, Redis), with strong emphasis on observability, idempotency, and scalable microservices. Successfully drove adoption of AI-assisted fraud classification by pairing transparency and manual overrides with measurable workflow improvements.”

“Built and deployed an enterprise GenAI knowledge assistant over thousands of internal PDFs/reports using a RAG stack (GPT-4 + Hugging Face embeddings + vector DB) to reduce manual search and SME escalations. Uses LangGraph/LangChain to orchestrate modular agent workflows with relevance filtering and fallback handling, and applies rigorous evaluation (golden datasets, edge cases, A/B tests) with production monitoring metrics.”

“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.”

“Integration engineer at ES Foundry who led deployment of ELsentinel, a production EL image-based solar cell quality monitoring system using a Swin Transformer classifier (>0.8 F1 across 15+ classes) plus a live real-time prediction dashboard. Strong in solving messy labeling/data-quality problems with process-team collaboration and shipping ML systems despite limited compute/infrastructure.”

“Engineer with banking and industrial/IoT experience who has deployed a payment-processing microservice with zero downtime, handling Protobuf schema evolution and sensitive data migration via dual-write/checksum techniques. Demonstrates strong cross-stack troubleshooting (pinpointed intermittent distributed timeouts to a failing ToR switch port) and customer-facing Python ETL customization using plugin-based parsers and Pydantic validation, plus hands-on monitoring/alerting improvements with operators.”

“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.”