Vetted Microservices Architecture Professionals

“Hands-on engineer focused on productionizing LLM-powered assistants: builds RAG pipelines with guardrails, response schemas, and citation-grounded outputs, then hardens them with explicit NFRs (latency, uptime, security, cost). Experienced diagnosing agentic/LLM workflow issues in real time using observability and stepwise isolation, and supports go-to-market via developer demos, workshops, and pre-sales technical evaluations in microservices/Spring Boot environments.”

“Backend-leaning full-stack engineer who has built and operated event-driven microservices platforms (FastAPI/React/TypeScript, Kafka, Kubernetes) and internal DevOps tooling. Delivered measurable impact through user-feedback-driven iteration (WebSockets update mechanism cutting redundant API calls ~30%) and operational improvements (deployment monitoring dashboard reducing rollback time ~40%), with strong focus on reliability, observability, and data consistency at scale.”

“Software engineer and University of Chicago graduate teaching assistant who built a full-stack internal analytics dashboard (React/TypeScript + Node/Express) and worked in RabbitMQ-based microservices with Prometheus/Grafana observability. Also created an AI-powered ERD diagram generator (React + MermaidJS + OpenAI) adopted by students to save hours on database assignments, using validation loops to ensure valid Mermaid output.”

“Full-stack engineer who has owned React/TypeScript + Spring Boot dashboard products end-to-end, including real-time performance/alerts and data aggregation across services. Strong in shipping MVPs quickly with feature flags, automated testing and CI/CD, and using monitoring/click-path analytics to prioritize work—achieved a 40% page-load reduction. Experienced operating microservices with RabbitMQ at scale, addressing retries/idempotency/observability and fixing duplicate-processing incidents with idempotent consumer patterns and DLQs.”

“Backend engineer with airline operations domain experience who modernized flight-ops systems from batch updates to real-time streaming on AWS (Kafka + Spring Boot microservices), improving latency and stability through metric-driven tuning and idempotency. Also shipped a production LLM decision-support component using RAG over operational logs and internal procedures, with strong guardrails and an evaluation/regression loop to reduce hallucinations and enforce grounding.”

“Built and shipped an AI-driven interview evaluation pipeline at SeekOut that automated recruiter screening via a multi-stage LLM agent workflow (.NET backend, RabbitMQ orchestration, Python workers). Emphasizes production-grade reliability (idempotency, retries, strict JSON/schema validation), strong observability with OpenTelemetry, and measurable efficiency gains including ~40% reduction in token usage/cost.”

Ambuk Rehani

Screened

Mid-level AI/Backend Engineer specializing in RAG and data platforms

Dallas, TX7y exp

“Built and shipped a production LLM-powered financial Q&A interface that extracts precise numeric data from PDFs using a hybrid AWS Textract + LLM normalization pipeline, with confidence gating and guardrails to prevent unreliable answers. Experienced with LangChain-based RAG orchestration (chunking, memory, structured outputs) and collaborated closely with PMs/analysts on IRS Form 990 extraction requirements.”

Sai Nikhil Kanchukatla

Screened

Senior Software Engineer specializing in Golang microservices and IAM/SSO

Dallas, TX6y exp

“Backend engineer with experience at DigitalOcean and BNY Mellon, specializing in secure, highly available authentication and API platforms. Built an enterprise SSO system integrating Okta via OIDC with resilience patterns (gRPC contracts, circuit breakers, Kafka) and strong encryption, and led a careful monolith-to-Golang microservices migration using shadow traffic, dual writes, and feature flags to preserve data integrity.”

Shrinivas Bhusannavar

Screened

Mid-level AI Engineer specializing in agentic LLM systems and RAG platforms

San Jose, CA5y exp

“Built and shipped Serrano AI, a multi-tenant SaaS conversational AI platform that automates Odoo ERP workflows and lets ops/finance/supply-chain teams query ERP data in natural language. Implemented a multi-agent architecture (LangChain/LangGraph/CrewAI) with hybrid RAG over ERP schemas, deployed on Heroku/Vercel with production observability, cutting reporting time by ~80% while addressing hallucinations, latency, and schema complexity.”

Xin Li

Screened

Staff Software Engineer specializing in distributed systems and high-concurrency APIs

Santa Clara, CA17y exp

“Full-stack engineer in an insurance + telematics org who built a 0→1 multi-agent, AI-driven mobile regression execution system integrating Jira and n8n, emphasizing determinism (schema-validated outputs), traceability (state-machine style step archives), and scalable device scheduling. Also designed an event-driven microservices architecture using Kafka with standardized API/event contracts and has hands-on incident response experience improving resilience via rate limiting, circuit breaking, caching, and idempotency.”

“Built practical automation systems spanning an NLP-based news classification pipeline and a WhatsApp interaction agent. Shows strong instincts around production reliability—using structured outputs, schema validation, idempotency, retries, and clarification flows to prevent bad actions in real-world messaging workflows.”

Almas Fathimah

Screened

Senior AI Engineer specializing in Generative AI and ML platforms

USA7y exp

“Built and owned a production RAG-based conversational AI system at Entera for real estate analysis, taking it from experimentation through AWS deployment, monitoring, and iterative improvement. Demonstrates strong practical judgment in retrieval design, LLM safety, and scalable Python service architecture, with measurable impact including 30-40% reduction in manual analysis time and roughly 30% better response accuracy.”

“Full-stack engineer with hands-on experience building and shipping production web products across AI, frontend, backend, and DevOps. Notably built an end-to-end resume-job matching platform during an internship that processed 1000+ resumes/day and cut recruiter screening effort by 60%, and later shipped an internal operations dashboard at CHS with measurable performance gains.”

TEJASWI GUDIMETLA

Screened

Mid-level Software Engineer specializing in backend systems, microservices, and AI pipelines

North Carolina, USA4y exp

“AI/LLM engineer focused on building reliable, scalable multi-agent and RAG-based pipelines across microservices. Stands out for combining practical experimentation with strong engineering discipline around schema validation, retries, observability, and structured API contracts to make LLM systems production-ready.”

Bhanu Akepogu

Screened

Mid Software Engineer specializing in backend, full-stack, and AI systems

USA3y exp

“Full-stack engineer with strong fintech/financial-systems experience who built an automated reconciliation and reporting system end-to-end (Python/Django/FastAPI + React, Docker on AWS), cutting reconciliation time by ~25% and improving audit traceability. Experienced designing typed REST APIs and Postgres data models, operating production workloads on AWS (EC2/Lambda/RDS/DynamoDB/CloudWatch), and building reliable ETL/integration pipelines with idempotency, retries, and reconciliation.”

“Full-stack engineer who has owned an operations/reporting dashboard end-to-end, spanning React/TypeScript frontend architecture, ASP.NET Core APIs, and SQL data access. Stands out for combining strong UI performance optimization with pragmatic backend decisions, post-launch monitoring, and 0→1 startup platform building that improved API speed by 35% while supporting 2,000+ transactions per hour.”

Anirudh Raj

Screened

Entry-Level Machine Learning & Cloud Engineer specializing in AI data pipelines

Boston, MA

“Early-career cloud/appsec-focused engineer with hands-on experience building secure, observable microservice systems on AWS (IAM least privilege, KMS encryption, Secrets Manager, CloudWatch, ALB) and troubleshooting autoscaling-related 500s down to connection pooling issues. Also deployed heavy ML workloads on Kubernetes by decomposing diffusion/transformer services, using workload identity to eliminate static credentials, and maintaining GitOps-style deployment audit trails.”

“Engineering leader for a facial-recognition edge platform who drove a full-stack modernization to scale from ~100 edge devices to thousands of locations. Led a major distributed-systems re-architecture (NATS streaming), database migration (MongoDB to PostgreSQL + TimeScaleDB), and UI/API modernization (React), while scaling the team 5→15 and implementing CI/CD + automated QA to improve release velocity and reliability.”

Luke Evers

Screened

Director-level engineering leader specializing in full-stack product and e-commerce systems

Remote12y exp

“Candidate is an aspiring founder currently exploring startup ideas and potential collaborators, with firsthand experience as an early employee at VC-backed companies, including joining a current company shortly after seed funding. They have not raised capital themselves but bring early-stage operating exposure and a strong desire to build something they own with a high-trust team.”

Lloyd Mangnall

Screened

Executive Technology Leader specializing in digital transformation and AI/ML platforms

30y exp

“Founder building AdaptiveWorX™ Flow, a graph-native, agent-first operating system intended to replace document-driven/Agile-era workflows with continuously verifiable, causally linked delivery. Brings decades of systems/architecture perspective plus prior digital health experience, including a PoC turning behavioral therapy sessions into structured longitudinal data using real-time A/V capture and multimodal AI, with an emphasis on compliance, traceability, and scalable platform design.”