Vetted Spring Boot Professionals

“Backend/platform engineer who owned a real-time customer analytics microservice stack in Python/FastAPI with Kafka streaming into PostgreSQL, including schema enforcement (Avro) and high-throughput optimizations. Strong Kubernetes + GitOps practitioner (EKS/GKE, Helm, Argo CD) who has handled CI/CD reliability issues with automated pre-deploy checks and rollbacks, and supported major migrations (on-prem to AWS; VM to EKS) with blue-green cutover planning.”

“Backend engineer with hands-on manufacturing/production-systems experience at Wallbox, improving the Supernova charger rework process by streamlining part-number/component updates. Strong in building modular Python/Flask services with clean integration layers (Cosmos DB, NetSuite, traceability/label printing), plus deep SQLAlchemy/Postgres performance tuning. Also brings scalable AI/ML integration and deployment experience (OpenAI/Hugging Face/TensorFlow Serving, Docker/FastAPI/Nginx) and multi-tenant schema isolation with RBAC.”

“Full-stack engineer specializing in Java Spring Boot microservices and React, with hands-on ownership of a merchant dispute management platform (security via RBAC/JWT, significant performance gains through SQL execution-plan-driven tuning and UI refactors). Also has experience at JPMorgan Chase optimizing high-volume financial-data services with API efficiency, caching, and async processing.”

“Full-stack developer who built an end-to-end Hotel Management System using React and Spring Boot with MongoDB and AWS. Has hands-on experience debugging API/data-fetching issues with Postman and validating results against the database, plus exposure to handling large data workloads with chunking and monitoring via Grafana/Tabula.”

“Backend engineer with hands-on experience designing and refactoring scalable Node.js/MongoDB systems and building Python/FastAPI services. Emphasizes production-grade security (JWT, refresh tokens, RBAC, Supabase Auth, RLS) and reliability practices like strong testing, monitoring, and rollback planning, including resolving concurrency and token/validation edge cases.”

“AI/ML engineer who built a production RAG-based internal document intelligence assistant (LangChain + Pinecone) to let employees query enterprise reports in natural language. Demonstrated hands-on pipeline orchestration with Apache Airflow and tackled real production issues like retrieval grounding and latency using tuning, caching, and token optimization, while partnering closely with non-technical business stakeholders through iterative demos.”

“Full-stack engineer who has shipped and owned production analytics dashboards using Next.js App Router + TypeScript, combining server components for data-heavy pages with client components for interactive charts/filters. Also built a Temporal-orchestrated payment reconciliation workflow with versioning, idempotency, and exponential-backoff retries, and has hands-on Postgres query/index optimization using EXPLAIN ANALYZE.”

“Backend/API engineer with ~4 years experience building production services in .NET Core/PostgreSQL/Redis/Docker and optimizing real-world latency issues (claims ~60% response-time improvement). Also built and owned an end-to-end RAG-based AI assistant using Python/FastAPI, OpenAI APIs, and Pinecone, plus agentic workflows with reliability guardrails (retries, confidence thresholds, monitoring). Currently pursuing a master’s degree and targeting a $150k base salary.”

Ganesh Medepalli

Screened

Mid-level Java Developer specializing in Spring Boot microservices and AWS

USA3y exp

“Backend engineer with primary experience in Java/Spring Boot microservices, AWS (EC2/ECS/Lambda), and CI/CD automation with Jenkins. Supported modernization/migration efforts at Berkshire Hathaway and Citius Infotech by containerizing legacy components with Docker, refactoring services to be stateless, and managing infra changes via Terraform and Git-based workflows; has limited but practical Python API prototyping experience (Flask/FastAPI) and solid conceptual grounding in Kubernetes and Kafka.”

Kiran M

Screened

Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices and data platforms

Bentonville, AR5y exp

“Backend/ML integration engineer with experience at Accenture and Walmart building Flask-based analytics and prediction APIs on PostgreSQL/MySQL. Strong focus on performance and scalability—uses precomputed aggregates, Redis caching, query tuning (indexes/partitioning/EXPLAIN), and async/background processing; also designs secure multi-tenant isolation with JWT and schema/db-per-tenant strategies.”

Pravalika Kasojjala

Screened

Mid-level AI/ML Engineer specializing in LLM, RAG/GraphRAG, and fraud analytics

Charlotte, NC5y exp

“LLM/agent engineer who has deployed a production internal assistant to reduce employee inquiry resolution time while maintaining regulatory compliance. Experienced with RAG, hallucination risk triage, and graph-based orchestration (LangGraph) for enterprise/banking-style workflows, emphasizing schema-validated, citation-backed, tool-constrained agent designs and tight collaboration with non-technical business/compliance stakeholders.”

Raviteja Kambhampati

Screened

Mid-level Full-Stack Java Developer specializing in cloud-native microservices and React

San Francisco, CA5y exp

“Full-stack engineer who has owned customer-facing analytics and dashboard products end-to-end using TypeScript/React with Spring Boot microservices. Strong in scaling and stabilizing distributed systems (RabbitMQ, DLQs/retries, observability with correlation IDs) and in building internal tooling that consolidates ELK/CloudWatch signals to speed up support and operations; reported ~30% performance improvement on a recent dashboard.”

Saicharitha Yanamandala

Screened

Mid-Level Software Developer specializing in Java, Cloud, and Microservices

Chicago, IL6y exp

“Backend/Python engineer who owned an end-to-end FastAPI + AWS internal natural-language document Q&A system (Textract extraction, embeddings/vector DB, LLM integration) with strong focus on reliability and latency. Hands-on with Kubernetes + GitOps (Argo CD, Helm, rolling updates/auto-rollback) and built/optimized Kafka streaming pipelines using Prometheus/Grafana. Also supported a zero-downtime on-prem to cloud migration with parallel run and gradual traffic cutover.”

Arthi R

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and cloud-native microservices

Remote – Washington, D.C.5y exp

“Backend engineer with fintech/banking experience (e.g., Canara Bank) building secure Python/Flask microservices for financial reporting and unified data access. Strong in Postgres/SQLAlchemy performance optimization (including materialized views) and in productionizing ML services on AWS (Lambda/ECS/CloudWatch) with Docker, model registries, and blue-green deployments, plus multi-tenant isolation via JWT-based middleware.”

Sri Chandana Topuri

Screened

Mid-level Java Full-Stack Developer specializing in Spring microservices and Angular

5y exp

“Backend-focused engineer working primarily with Python/Django who also handles full-stack responsibilities. Has hands-on experience deploying containerized Python/Java microservices to Kubernetes using Helm and GitOps (ArgoCD), plus building Kafka-based event streaming with reliability controls (acks, consumer groups, DLQ). Also supported major on-prem to cloud/hybrid migrations using Terraform/Ansible with blue-green cutovers and data replication to minimize downtime.”

Sanjay Mandru

Screened

Mid-Level Full-Stack Software Engineer specializing in cloud microservices and real-time analytics

Buffalo, NY3y exp

“Software engineer who built a reusable React component package (UI modules, auth helpers, API client wrappers) for an AI SaaS background-removal project, emphasizing performance (tree shaking/dynamic imports) and reliability (Jest + Storybook). Also delivered a unified REST API for Samsung Big Data Portal, resolving cross-team issues by standardizing schemas, improving validation/logging, and operating effectively amid shifting requirements.”

Weston Carpenter

Screened

Mid-level Backend Software Engineer specializing in distributed systems

San Francisco, CA6y exp

“Technical/presales engineer with experience at Grubhub and Appen, spanning LLM-adjacent data labeling workflows and production AI troubleshooting. Built an integrations platform at Grubhub and has hands-on experience diagnosing prompt-related AI failures via Splunk, adding JUnit tests and logging to prevent recurrence. Known for shipping customer-specific workflow adaptations (e.g., OCR annotation coordinate transformations for crop/rotation) while keeping timelines intact through iterative delivery and parallelization.”

“Built and productionized LLM-driven content intelligence/SEO agents for a high-traffic media platform, automating tagging/summarization/metadata with FastAPI + async orchestration and strict JSON-schema outputs. Demonstrated measurable impact (40% faster publishing, +20% organic traffic in 3 months) and strong reliability practices (offline evals, shadow mode, canaries, fallbacks, idempotency, and monitoring).”

“LLM/agent engineer with production experience in the insurance claims domain, integrating OpenAI + LangChain into a claims platform to automate unstructured document extraction/classification and cut manual effort by 35%. Built reliable, fault-tolerant AWS/Kubernetes microservices with CloudWatch monitoring plus circuit breakers/retries/fallbacks, and implemented multi-step Spring Boot orchestration with schema validation, confidence gating, and human-in-the-loop handling for low-confidence cases.”

“Data engineer/platform builder who has owned production pipelines end-to-end processing millions of records/day, with strong emphasis on data quality (quarantine workflows) and reliability (monitoring, retries, incremental loads). Also designed large-scale external data collection/crawling with anti-bot handling and backfills, and shipped versioned REST data services optimized for performance and developer usability in an early-stage environment.”

Ashwini Ramesh Kumar

Screened

Junior AI Software Engineer specializing in LLMs, RAG, and agent workflows

Remote1y exp

“Backend/ML-leaning engineer who built a content-based event recommender for FlowMingle using embeddings + HNSW vector search on Google Cloud, with Firebase as the backend and a managed recommendation lifecycle (15 recs/user, daily async generation, weekly deletion) now serving 1500+ users. Also led a cost-driven migration of ConvAI services to Azure AI using parallel request testing from a Unity client, with post-migration monitoring via logs and model evals; contributed to a Massachusetts law-enforcement conversation analysis system by expanding ingestion to PDF/TXT/Excel and multi-file inputs.”

Shruthi Nandakumar

Screened

Mid-level Full-Stack Developer specializing in FinTech and cloud-native microservices

St. Louis, MO5y exp

“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.”

Keerthi Kalluri

Screened

Senior Full-Stack & GenAI Engineer specializing in healthcare and financial services

6y exp

“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).”

Shweta Gupta

Screened

Senior Backend Software Engineer specializing in Java microservices, Kafka, and AWS

Seattle, WA6y exp

“AI engineer who shipped a production chat assistant for a storage company by building the underlying RAG-style knowledge base (document ingestion, chunking/embeddings, FAISS vector store) and an admin update interface to keep content current. Also has full-stack delivery experience (Python REST APIs + React/TypeScript UI) and AWS operations using Terraform/Jenkins, including handling a real production performance incident by optimizing DB queries and adding auto-scaling.”