Vetted Prometheus Professionals

“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”

“At HPE, led and deployed an enterprise-grade LLM document intelligence platform for an insurance client, automating extraction from highly variable PDFs/scans/emails and raising field accuracy from 74% to 93%. Built a LangChain/Pinecone/OpenSearch RAG framework to cut hallucinations by 37% and operationalized LangSmith evals in CI, driving a 41% triage accuracy lift and >33% fewer incorrect resolutions while partnering closely with claims operations via HITL workflows.”

“ML/LLM engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.”

“Software engineer at Procter & Gamble focused on warehouse/operations systems, building near-real-time order/inventory visibility using Java/Spring Boot, React, Kafka, PostgreSQL, and Redis with measurable latency and load-time gains. Also shipped internal LLM/RAG knowledge assistants grounded in company runbooks and workflows, implementing guardrails and an evaluation loop that drove concrete retrieval improvements (document chunking) and regression prevention.”

“Data/MLOps engineer (Cognizant background) who owned an AWS/Airflow/Snowflake healthcare transactions pipeline processing ~8–10M records/day and cut pipeline/data-quality incidents by ~33%. Also built and deployed a production FastAPI model-inference service on Kubernetes (Docker, HPA) with strong observability (Prometheus/Grafana), versioned endpoints, and resilient backfill/idempotent external data ingestion patterns.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

“Backend/DevOps engineer pivoting into robotics/space, building hands-on ROS2 (Humble) skills via Gazebo simulations and experimenting with Nav2 and slam_toolbox. Brings strong distributed-systems and real-time debugging practices (profiling, instrumentation, QoS/retry patterns) and is actively learning perception and control fundamentals to transition into autonomous robotics.”

“Internship experience at Qualitest building and deploying an LLM-powered test automation system that reduced manual test creation and improved efficiency (~40%). Demonstrates strong production engineering for LLM systems (timeouts/retries/monitoring/caching, prompt optimization, batching) and has scaled workflows to 100+ concurrent jobs; also has orchestration experience with AWS Step Functions and Kubernetes.”

“Frontend-focused engineer with experience at T-Mobile and U.S. Bank who maintained a TypeScript utility library (types, tests, build pipeline, and docs) adopted by multiple teams, and improved React workflow performance by refactoring components and optimizing data fetching. Known for pragmatic cross-team support—reproducing issues quickly, shipping well-tested fixes, and managing changes carefully to avoid breaking downstream apps.”

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“Software engineer with experience at Walmart and Amex building customer-facing backend services and microservices at scale (RabbitMQ). Built an internal developer tooling platform integrating Figma with GitHub Copilot to automate consistent React component creation, adopted across multiple teams; emphasizes fast, safe iteration using metrics, feature flags, gradual rollouts, and automated testing.”

“Full-stack engineer with hands-on experience building a large-scale healthcare claims and provider-enrollment system end-to-end (React frontend, Spring Boot microservices, PostgreSQL on AWS). Optimized high-volume claims processing (millions of records/day) using indexing/pagination and asynchronous workloads via AWS Lambda/Kafka, and deployed containerized services with Docker/Jenkins on AWS.”

“Backend/AI engineer with experience in both high-scale financial services (JP Morgan trade compliance analytics API on Java/Spring Boot/Postgres/Elasticsearch on AWS EKS processing 1M+ trades/day) and applied LLM systems for legal research (LangChain/OpenAI + Weaviate semantic search). Demonstrated strength in reliability/performance engineering, data consistency during migrations, and production-grade workflow orchestration with observability and human-in-the-loop guardrails.”

“Backend engineer who built Flask-based microservices for a high-throughput risk engine, using Kafka for streaming decoupling and Redis for low-latency caching, with PostgreSQL + Cassandra for mixed relational and time-series needs. Has hands-on experience productionizing ML inference (Azure OpenAI/TensorFlow) behind REST APIs with async queues, batching, and caching, plus multi-tenant isolation via schema separation and RBAC with per-tenant rate limiting.”

“Maintained and improved a set of JavaScript packages at JPMorgan, including a major refactor of an event-emission layer used in LLM streaming that delivered ~20% runtime speedup with no API changes. Known for a measurement-driven performance approach (profiling + simplification + validation), strong backward-compatibility discipline, and documentation that accelerates internal adoption and reduces support questions.”

“Backend/data engineering candidate specializing in Python/Flask services and ML-enabled systems, deploying containerized workloads on AWS ECS/EKS with strong observability (Prometheus/Grafana) and PostgreSQL performance tuning. Built multi-tenant architectures with row- and schema-level isolation and optimized a Kubernetes-based Airflow + Spark nightly ETL pipeline for an e-commerce client, improving performance by 250%+ and reliably beating morning reporting deadlines; also contributed to Apache Airflow (SQLAlchemy/PostgreSQL area).”

“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”

“Software engineer with Citi Bank experience building real-time fraud validation/scoring for loan processing, spanning Spring Boot microservices and a FastAPI Python service secured with OAuth2/JWT. Delivered React/TypeScript operations dashboards and deployed containerized services via Docker/Kubernetes with Jenkins CI/CD, while also tuning databases (Oracle/Postgres) and handling high-volume latency/scaling issues using ELK, caching, and autoscaling on AWS.”

“Cloud/Kubernetes-focused production engineer with experience running 99.95% uptime platforms across AWS/Azure/GCP. Strong in incident response and performance troubleshooting (including a 30% MTTR reduction), and in building secure CI/CD and Terraform-based IaC for AKS/GKE microservices with robust change controls and rollback practices. Notably does not have direct IBM Power/AIX/VIOS/HMC or PowerHA/HACMP ownership.”

“Senior software engineer with Amazon experience who owned end-to-end improvements to a real-time payment authorization service, rebuilding it as a reactive Spring WebFlux microservice with saga orchestration and Kafka event streaming, deployed on AWS EKS with strong observability. Also built React+TypeScript and Node/Express full-stack workflow apps (onboarding, campaign management, admin review) and has experience shipping quickly in ambiguous startup environments while maintaining reliability and data correctness.”

“Backend/AI engineer who owned a high-scale Java/Spring Boot microservice for a financial application (millions of requests/day) and led major reliability/performance fixes (including ORM/query and PostgreSQL tuning) achieving ~60% latency reduction. Also shipped application-layer LLM features for ops teams (summarization + tool-calling) with strong guardrails (PII redaction, validation, audit/feedback) and designed a state-driven agent workflow with retries, circuit breakers, and human escalation.”

“Built and deployed a production LLM-powered customer support assistant using a RAG backend in Python, focused on deflecting repetitive Tier-1 tickets and reducing resolution time. Demonstrates strong production engineering instincts around reliability (confidence scoring + human fallback), scalability/cost optimization (multi-stage pipelines), and workflow orchestration/observability (LangChain, custom DAGs, structured logging, step metrics).”