Sainath R

Build Fix Repeat

Backend, Data & Applied AI.

# Open to New Roles💼 10+ years experience

Software engineer with 10+ years building production Java services and distributed systems. Specializing in AI-assisted workflows, backend architecture, and operational automation.

Photo of Sainath R
Applied AIJavaSpring BootDistributed SystemsMicroservicesData EngineeringCloud

What I build

🤖 Retrieval-grounded AI systems

Building AI experiences that answer from policy, claims context, and operational evidence instead of free-form guesses.

RAG · Gemini · Vertex AI · Google ADK

⚙️ Java backend systems

Designing resilient services and event-driven architectures for complex, long-running processes.

Java · Spring Boot · Kafka · RabbitMQ

🧰 Data engineering & platforms

Building reliable datasets, pipelines, and internal tools that make teams faster and production operations clearer.

Python · Databricks · Spark · Airflow · GraphQL · Kubernetes

Selected systems

🧠 RAG-powered claims guidance assistant

Applied AI · Healthcare
Problem

Claim reviewers search policy documents by hand, while ungrounded models cannot be trusted with benefits answers.

Approach

A retrieval-first pipeline that grounds Gemini answers in cited policy and claim evidence, with human review on every output.

RAG · Gemini · Vertex AI SDK · Google ADK · Healthcare rules

🤖 AI-assisted business-rule automation

Applied AI · Healthcare
Problem

High-volume routine cases consume reviewer bandwidth, while ambiguous cases still require judgment.

Approach

Gemini and ADK evaluate business rules to resolve standard cases automatically and escalate exceptions for review.

Gemini · Vertex AI SDK · Google ADK · ReactJS

📊 Member data platform

Data · Healthcare
Problem

Accumulator data spanned multiple systems, complicating consistent analytics and reporting.

Approach

A Databricks + Spark SQL source of truth feeding Looker datasets and Airflow-driven daily client reports.

Databricks · Spark SQL · Airflow · Looker

⚡ Event-driven Java service platform

Backend · Distributed systems
Problem

Long-running business processes span services that must coordinate reliably.

Approach

Spring Boot microservices communicating over Kafka and RabbitMQ, designed for asynchronous failure and replay.

Java · Spring Boot · Kafka · RabbitMQ · Kubernetes

🧰 Developer operations platform

Internal platform · Production tooling
Problem

Operational scripts lived in scattered locations with no safe, consistent way to run them.

Approach

A unified Flask console with reusable execution patterns, rollback support, and full audit trails.

Python · Flask · ReactJS · Data remediation

🕸️ Network graph and pathfinding service

Telecommunications · Network provisioning
Problem

Network provisioning depends on viable routes through complex device topologies.

Approach

A Neo4j device graph with a REST API for Yen's K-shortest-path queries.

Neo4j · Java · Spring Boot · REST APIs · Graph algorithms

Core stack

Languages $ stack --languages 5

JavaKotlinPythonSQLShell scripting

Backend & Platform $ stack --backend-platform 10

Spring BootFlaskKafkaRabbitMQHibernateLiquibaseActivitiPlay FrameworkExpressJSGraphQL

Applied AI $ stack --applied-ai 12

Vertex AI SDKGemini modelsGoogle ADKLangChainRAGRagasVertex AI EvaluationPrompt engineeringAgentic workflowsAI-assisted code generationDocument classificationRule evaluation

Frontend $ stack --frontend 5

ReactJSHTMXJavaScriptHTMLCSS

Data $ stack --data 13

DatabricksSpark SQLAirflowLookerPostgreSQLOracleMySQLMongoDBNeo4jRedisApache SolrpandasNumPy

Cloud & DevOps $ stack --cloud-devops 12

AWSDockerKubernetesJenkinsGitLinuxSplunkGrafanaSonarQubeFortifySAMLOAuth 2.0

Career timeline

Lead Software Engineer

August 2022 - Present

Collective Health·Healthcare benefits and claims administration·Applied AI, Backend & Data·Dallas, TX

JavaSpring BootKafkaRabbitMQGeminiVertex AILangChainPythonReactJSExpressGraphQLDatabricksAirflowLooker
  • Well-versed in the Applied-AI stack: Gemini, Vertex AI, Google ADK, RAG retrieval.
  • Runs the team's Flask tooling hub: remediation, test data, and prod support in one auditable UI.
  • Ships claims visibility: an Express/GraphQL dashboard across every processing state.
  • Built the Databricks + Spark SQL source of truth for accumulator data.
  • Automates daily client reporting with Airflow over warehouse queries into Looker datasets.
  • Builds and runs Spring Boot adjudication + X12 837 services on Kafka/RabbitMQ — DLQ replay, Grafana alerts, rollback tooling.

Software Engineer

October 2021 - August 2022

Glueup.com (Event Bank)·Events, CRM, webinars, and networking·Backend·McLean, VA

KotlinSpring BootPlay FrameworkMongoDBjOOQLiquibaseMaven
  • Rewrote Play APIs in Kotlin + Spring Boot.
  • Database modules on Maven/Liquibase; shared jOOQ DAOs.
  • MongoDB dynamic-property APIs with TDD design docs.

Software Engineer

April 2018 - September 2021

Verizon·Telecommunications and network provisioning·Backend & Network Provisioning·Cary, NC

JavaSpring BootKafkaNeo4jHibernateActivitiKubernetesFortify
  • Created the Spring Boot template the org builds services from.
  • Moves provisioning events over Kafka; async APIs for long-running flows.
  • Graphs network devices in Neo4j; serves Yen's shortest-path routing.
  • Cleans Fortify findings; locks transactions against activation races.
  • Pipelines to Kubernetes; shared Hibernate libs; AOP audit layer.

Application Developer

October 2015 - March 2018

General Electric·Energy and pipeline operations·Full-Stack·Overland Park, KS

JavaSpring BootAWS EC2OraclePostgreSQLApache SolrSAMLJenkins
  • Wired SAML SSO for internal + external providers.
  • Lifted lower envs to EC2 with scripted on-demand test envs.
  • Slashed log noise with new logging standards.
  • Solr search on Spring Boot, refreshed from Oracle on schedule.
  • Moved services off Karaf; Jenkins pipelines.
// Thanks — Sainath; 0 errorscd29d5d · 2026-09-22