SDE · Broadridge · Hyderabad, IN
Open to SDE-2 Roles
Mayank Sahu
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Java · Spring Boot · Distributed Systems · AI Infrastructure
Designing systems that scale, APIs that hold, and agents that remember.
3+ years engineering production backend services at Broadridge — microservices, event-driven pipelines, and on-call reliability for 10,000+ enterprise users. Now extending into AI infrastructure: agent memory, RAG pipelines, and multi-agent orchestration.
Featured System
Continuum
Active · v1Python · OpenAI API · Vector DBs · Redis · Kafka · AWS · RAG
Agent memory is a hard problem — LLMs have no native persistence across sessions. Continuum solves this with a layered STM / MTM / LTM architecture that routes memory by recency, relevance, and durability.
- Architecture: Three-tier memory hierarchy (Short-Term in Redis, Mid-Term in-process, Long-Term in Vector DB) with lifecycle-aware routing logic.
- Pluggable Backend: Vector-store interface abstracts the embedding provider — swap Pinecone, Chroma, or Weaviate without touching routing logic.
- Pipeline: Kafka-driven MTM-to-LTM consolidation pipeline for durable memory promotion without blocking the agent loop.
memory.architecture
STM
Redis · <1h TTL · Hot Context
↓ promote
MTM
In-Process · Session Scope
↓ consolidate via Kafka
LTM
Vector DB · Embedding Search
Experience
Software Engineer
Broadridge IndiaPlatform · Reliability · Productivity
- Engineered backend microservices in Java + Spring Boot backed by MySQL, serving 10,000+ enterprise users across financial workflows — reliability and scalability at the core.
- Optimized REST API performance and inter-service communication, reducing aggregate latency by ~45% and improving service scalability by 3x.
- Integrated Kafka and async messaging for high-throughput service-to-service flows, enabling event-driven patterns across distributed backend services.
- Production on-call rotation on AWS — debugged across services, queues, and infra; reduced Mean Time To Detect (MTTD) by 50%.
- Designed and operated Jenkins + Docker CI/CD pipelines for 15+ microservices at 99.5% uptime; Python automation frameworks eliminated 60% of manual ops.
- Drove regression coverage to 85% via JUnit / PyTest, cutting release cycle time from 2 days to 4 hours.
Product Engineering Intern
HighRadiusFull-Stack · AI · Fintech
- Built full-stack features (React + Node.js + MongoDB) for 500+ corporate clients across 3 Agile releases.
- Developed AI invoice-matching model (Python + scikit-learn), reducing manual processing by 80%.
Technical Stack
Languages
Backend & Systems
Databases & Caching
Messaging & Events
Cloud & DevOps
AI Infrastructure
Reliability
Testing & Quality
Education
Siksha 'O' Anusandhan University
B.Tech · Computer Science & Engineering · CGPA 8.66 / 10