Professional Summary
Backend Software Engineer with nearly 3 years building scalable, low-latency microservices in Java and Spring Boot. Strong in distributed systems, Kafka-based event-driven architecture, and large-scale data pipelines (Databricks/PySpark, BigQuery). Delivered 35-60% cost reductions, handled 800K+ QPS serving 100M+ users, and built reusable frameworks adopted across multiple teams.
Pursuing an M.Tech in AI & ML (BITS Pilani) and building toward production ML and data-platform engineering — with hands-on work in RAG pipelines, LLM-powered services, and end-to-end MLOps. Architect high-performance systems with sub-30ms p95 latency, zero-downtime migrations, and comprehensive observability.
Technical Skills
Languages
Java, Python, SQL, JavaScript
Backend & Frameworks
Spring Boot, Kafka, Kafka Streams, gRPC, Protobuf, REST APIs
AI & LLMs
LLMs, RAG, Vector Search (ChromaDB), Prompt Engineering, HuggingFace Transformers, PyTorch, scikit-learn, MLflow, FastAPI
Data Processing
Databricks, Apache Spark, PySpark, ETL Pipelines, Batch Processing
Databases & Caching
Redis, Aerospike, PostgreSQL, MySQL, ClickHouse, BigQuery, AlloyDB
Infrastructure & Observability
Kubernetes, Docker, Helm, Terraform, GCP (GKE, GAR), CI/CD, Prometheus, Grafana
System Design
Microservices, Event-driven Systems, Circuit Breaker, Dual-Write, API Versioning, Distributed Tracing
Education
M.Tech. Artificial Intelligence & Machine Learning (April 2026 - Present)
BITS Pilani — Work Integrated Learning Programme
Bachelor of Technology in Computer Science (CGPA: 9.10 / 10.00) 🥈 Silver Medalist
Rajasthan Technical University — Aug 2019 to July 2023
Experience
Sept 2023 – Present
Software Development Engineer (SDE-1), Backend
Glance, InMobi — Bengaluru, IN
- Architected and built a content-enrichment microservice (Java/Spring Boot) handling 800K+ QPS at sub-30ms p95 latency, serving 100M+ users (Kafka, Protobuf, Redis, Aerospike)
- Designed a dual-write architecture with a protocol adapter, enabling zero-downtime migration and integration of new data sources across distributed systems
- Migrated Python services to Java/Spring Boot and built L0 personalization pipelines for large-scale content ranking
- Optimized Databricks/PySpark pipelines, cutting compute cost 35% via DataFrame caching; diagnosed and fixed a production memory leak through heap-dump analysis
- Built a gRPC-based dynamic configuration service for 10+ microservices, enabling semantic versioning for backward-compatible rollouts
- Created a reusable observability framework (gRPC interceptors + Prometheus) adopted across 5+ services, and a cache layer cutting API cost 60%
- Built a GDPR-compliance API with region-based access controls, batch processing of 10K+ records, and automated monitoring
- Deployed a release-automation platform (Python/Flask) on GKE with Helm and CI/CD, cutting deployment time 90%; managed Protobuf versioning across 10+ services
Tech: Java, Spring Boot, Kafka, Kafka Streams, gRPC, Protobuf, Redis, Aerospike, PostgreSQL, ClickHouse, BigQuery, Databricks, PySpark, Kubernetes, Docker, Helm, Terraform, GCP (GKE, GAR), Prometheus, Grafana
May 2022 – July 2022
Machine Learning Engineering Intern
Celebal Technologies — Jaipur, IN
- Built a video recommendation system on user-interaction data, lifting click-through rate (CTR) by 1.4%
Tech: Python, Machine Learning, Recommendation Systems
Projects
Dockerized Spring Boot REST Service
Java / Backend — github.com/PranavSagar/docker-java-backend
- REST API persisting to MongoDB via Spring Data, run alongside its database through Docker Compose with service networking and a persistent volume
Tech: Java 21, Spring Boot, Spring Data MongoDB, Docker, Docker Compose
Content Intelligence Pipeline
AI / MLOps — github.com/PranavSagar/content-intel-pipeline
- News-classification service: fine-tuned DistilBERT to 94.64% on AG News, served over FastAPI with Kafka streaming, MLflow experiment tracking, and Evidently drift checks; deployed on HuggingFace Spaces
Tech: Python, PyTorch, HuggingFace, FastAPI, Kafka, MLflow, Evidently, Docker
RAG Football Wiki
AI / RAG — github.com/PranavSagar/rag-football-wiki
- Retrieval-augmented Q&A service: sentence-transformer embeddings in a ChromaDB vector store, retrieved as context for an LLM (Claude) to generate grounded answers; served via FastAPI
Tech: Python, LLM (Claude), sentence-transformers, ChromaDB, FastAPI
Achievements
🏆 Avengers Award at Glance (InMobi)
🥈 Silver Medalist - Rajasthan Technical University
📜 Microsoft Certified - Azure AI & Data Fundamentals
💻 LeetCode Rating 1577 (Top 25%)
🥈 Smart India Hackathon 2022 Runner-Up