Sahil Shinde
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SOFTWARE ENGINEER — PUNE, INDIA

Hi, I'm Sahil.I build backend systems and intelligent products.

Now

Programmer Analyst (Java Backend Developer) at Avanzens Consultancy Services

Open to full-time engineering roles — remote or on‑site.

I work across Java, Spring Boot, distributed backend systems, React, and measured RAG pipelines. Based in Pune, India, with 2 years of experience and open to globally distributed engineering teams.

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  • Java / Spring Boot
  • React / TypeScript
  • Distributed Systems
  • Applied AI / RAG
ISelected Work

Selected Work

Backend systems and retrieval engineering, presented through decisions, architecture, and measured outcomes.

1Backend Systems / Distributed Systems

Claims Processing System

A Spring Boot claims workflow built around controlled state transitions, secure role boundaries, idempotent operations, caching, and asynchronous status processing.

82.5%

Reporting query latency reduction

58.3 ms10.2 ms

Benchmark result; not production traffic.

~18–24%

p95 claim-read latency improvement

Measured with Redis in a 100K-claim benchmark; not production traffic.

Key engineering
  • Controlled claim lifecycle transitions
  • Idempotency and audit history
  • Kafka asynchronous status processing
  1. Claim request — JWT-authenticated REST request
  2. Spring Security — JWT role-based access control
  3. Claims service — Spring Boot REST API
  4. Lifecycle rules — Controlled state transitions and idempotency
  5. PostgreSQL — Claims data managed with Flyway
  6. Redis — Claim-read cache · linked to Claims service
  7. Apache Kafka — Asynchronous status processing
  8. Status processor — Consumes asynchronous status work
  9. Audit history — Records claim changes
Secure claim requests follow controlled lifecycle transitions, persist to PostgreSQL, use Redis for reads, and publish asynchronous status work through Kafka with an audit trail.
Claims processing flow
  1. Claim request
  2. Spring Security
  3. Claims service
  4. Lifecycle rules
  5. PostgreSQL
  6. Redis
  7. Apache Kafka
  8. Status processor
  9. Audit history
Stack
  • Java
  • Spring Boot
  • PostgreSQL
  • Flyway
  • Spring Security
  • JWT
  • Redis
  • Apache Kafka
  • JUnit 5
  • Mockito
  • Testcontainers
  • Docker
  • GitHub Actions
2Applied AI / RAG

RagLens Evaluation-First RAG System

A retrieval pipeline that narrows, reranks, and evaluates evidence instead of treating a generated answer as proof that retrieval worked.

Many RAG projects stop after generating an answer. RagLens keeps retrieval evaluation inside the engineering process.

0.5680.792

Context Precision

83.3%100%

Evidence hit rate

Evaluation result for the RagLens retrieval pipeline.

Key engineering
  • Qdrant retrieval and metadata filtering
  • CrossEncoder reranking
  • RAGAS evaluation
  1. PDF — Source document
  2. Chunking
  3. Embeddings — 384 dimensions
  4. Qdrant retrieval — Up to 60 candidates
  5. Metadata filtering
  6. MMR — Up to 40 candidates
  7. CrossEncoder — Up to 8 final chunks
  8. LLM context — Final context chunks
  9. RAGAS evaluation — Measures retrieval results
Documents become 384-dimensional embeddings, retrieval produces up to 60 candidates, MMR keeps up to 40, and CrossEncoder reranking selects up to 8 context chunks before evaluation.
RagLens retrieval and evaluation pipeline
  1. PDF
  2. Chunking
  3. Embeddings
  4. Qdrant retrieval
  5. Metadata filtering
  6. MMR
  7. CrossEncoder
  8. LLM context
  9. RAGAS evaluation
Stack
  • FastAPI
  • LangChain
  • Qdrant
  • HuggingFace
  • CrossEncoder
  • RAGAS
  • React
3Full Stack

Job Portal

A full-stack hiring platform where employers post jobs and move applicants through interview stages, built on a stateless JWT-secured Spring Boot API, MongoDB, and a React + Redux client.

4

Application states

Applied, interviewing, offered, and rejected, tracked per applicant.

3

Account roles

Applicant, employer, and admin, each with its own routes.

Key engineering
  • Stateless JWT authentication with Spring Security and BCrypt-hashed passwords
  • Application lifecycle: applied → interviewing → offered or rejected
  • In-app notifications when a job is posted or an interview is scheduled
  1. React client — Redux state and role-based routes
  2. Axios interceptor — Adds Bearer JWT; 401 returns to login
  3. JWT filter — Stateless Spring Security
  4. REST API — Auth, users, profiles, jobs, notifications
  5. User service — Registration and BCrypt passwords
  6. Users and profiles — MongoDB collections
  7. Job service — Post, apply, and change application status
  8. Jobs and applicants — Applicants embedded in each job
  9. Notification service — Unread and read in-app notifications
  10. Notifications — MongoDB collection
A React client sends Bearer-token requests through a stateless JWT filter to Spring Boot services; applications move through status changes stored with each job in MongoDB, and key events create in-app notifications.
Job portal request flow
  1. React client
  2. Axios interceptor
  3. JWT filter
  4. REST API
  5. User service
  6. Users and profiles
  7. Job service
  8. Jobs and applicants
  9. Notification service
  10. Notifications
Stack
  • React
  • Redux Toolkit
  • Mantine
  • Spring Boot
  • Spring Security
  • JWT
  • MongoDB
IIAbout

I like understanding what happens between an HTTP request arriving and a reliable response leaving.

That leads me into
  1. API design
  2. Database behaviour
  3. Security boundaries
  4. Message flows
  5. Testing
  6. Performance

More recently, I've applied the same approach to retrieval and LLM systems, especially the parts that can be measured. React gives me enough frontend range to carry features across the stack.

IIIPrinciples

Engineering is mostly decisions.

  1. Design the state transition before writing the endpoint.

    In practiceExplicit claim lifecycle rules control state transitions.

    Claims Processing System

  2. Measure before optimizing.

    In practicep95 claim reads ~18–24% faster with Redis in a 100K-claim benchmark.

    Claims Processing System

  3. Make failure behaviour explicit.

    In practiceIdempotent operations, with an audit history of claim changes.

    Claims Processing System

  4. Authentication is only useful when authorization is correct.

    In practiceJWT role-based access control behind secure role boundaries.

    Claims Processing System

  5. Tests should verify boundaries, not implementation details.

    In practiceIntegration tests with JUnit 5, Mockito and Testcontainers, run in CI.

    Claims Processing System

  6. An AI answer is not evidence that retrieval works.

    In practiceRAGAS context precision measured: 0.568 → 0.792.

    RagLens
IVCapabilities

A practical technical range.

Grouped by the part of a system they serve, with the projects and roles where each group is used.

Backend
  • Java
  • Spring Boot
  • REST APIs
  • Spring Security
  • JPA/Hibernate

Used inClaims Processing SystemJob PortalAvanzens Consultancy ServicesInternMeets

Data & Messaging
  • PostgreSQL
  • MongoDB
  • Redis
  • Kafka

Used inClaims Processing SystemJob PortalAvanzens Consultancy Services

Frontend
  • React
  • TypeScript
  • JavaScript

Used inRagLensJob PortalInternMeets

Testing & Delivery
  • JUnit
  • Mockito
  • Testcontainers
  • Docker
  • GitHub Actions

Used inClaims Processing System

Applied AI
  • FastAPI
  • LangChain
  • Qdrant
  • HuggingFace
  • RAGAS

Used inRagLens

VExperience

Freelance, internship, and backend engineering.

  1. Sep 2025Present

    Full-timeCurrent

    Avanzens Consultancy Services

    Programmer Analyst (Java Backend Developer) · Pune

    • Spring Boot services for user management and credit-scoring workflows, with secure REST APIs.
    • Kafka-based asynchronous credit-score updates, decoupling core processing from downstream notifications.
    • Controlled real-broker benchmark: ~7.4K events/sec median throughput and 39 ms p95 producer-to-consumer latency across 3,000 events.
    • Tested failure and retry scenarios; evaluated DLQ handling, consumer idempotency, and database-to-Kafka consistency.
    • Java
    • Spring Boot
    • Kafka
    • PostgreSQL
    • Flyway
    • JWT
    • Spring Cloud Gateway

  2. Mar 2025Aug 2025

    Internship6 mos

    InternMeets

    Software Engineer Intern (Full-Stack) · Pune

    • Spring Boot REST APIs and a React + TypeScript UI for patient-record and appointment workflows in a clinic application.
    • Role-based access control for patient, doctor, and admin roles across API endpoints and frontend routes.
    • Optimized frequently used MySQL queries through query tuning and indexing.
    • Spring Boot
    • React
    • TypeScript
    • Spring Security
    • JWT
    • MySQL

  3. Oct 2023Mar 2024

    Freelance6 mos

    Self-Employed

    Freelance Software Developer · Pune

    • ServiceNow workflow automation for incident and request management.
    • ServiceNow
    • Flow Designer
    • Business Rules
    • Client Scripts
    • ACLs
    • REST integrations
VIEducation

Education and certifications.

  1. Aug 2024Feb 2025

    Post Graduate Diploma in Advanced Computing (PG-DAC)

    C-DAC, Pune

  2. Aug 2019Jun 2023

    Bachelor of Engineering, Information Technology

    Sinhgad College of Engineering, Pune

Certifications

  • Oracle Cloud Infrastructure 2025 Certified Foundations Associate
  • ServiceNow Certified System Administrator (CSA)
  • 500+ DSA problems solved across LeetCode, InterviewBit and GeeksforGeeks
VIIContact

Have a system worth building? Let's talk.

For engineering roles and thoughtful collaboration, email is the best place to start.

  • Backend
  • Full Stack
  • Applied AI
Based in
Pune, India (IST, UTC+5:30)
Open to
Full-time engineering roles — remote or on‑site.
Experience
2 years
Or send a message here