Morgan Stanley (via Wissen) · Led 3-person Airflow rollout · 40% faster P&L reporting
Hi, I'm Ashirvad. I architect production-grade distributed systems optimized for reliability and scale — bridging heavy data engineering with agentic AI and automated workflows.
Technologies I work with
With 2+ years of experience building production-grade enterprise systems, my expertise lies in heavy backend architecture and robust data engineering. I focus on developing resilient, distributed services and moving beyond basic API wrappers to solve complex, high-scale engineering challenges.
My core specialty sits at the intersection of high-volume data processing and autonomous systems. I architect robust ETL pipelines, optimize data streams, and integrate Agentic AI to automate complex workflows and drive intelligent decision-making at scale.
By combining a rigorous foundation in data structures and algorithms with modern system design principles, I architect solutions that are not just functional, but highly performant, maintainable, and built to withstand enterprise-level loads.
"Great engineering should feel invisible. I build heavy-lifting systems behind the scenes so that complex data and intelligent workflows can just... work."
System Design Snapshot — PnLGuard
Ingest → Rule Engine → Redis Cache → GenAI Explainer → HITL Review → Audit Store
Decoupled detection from AI explanation so rule-based breaks stay deterministic while GenAI adds risk context for human reviewers.
Building resilient, highly scalable microservices and distributed systems utilizing robust frameworks like Spring Boot.
Designing fault-tolerant ETL pipelines, optimizing complex database queries, and managing distributed data processing engines.
Seamlessly integrating intelligent, autonomous agents and LLM capabilities into production systems to automate complex enterprise tasks.
Architecting high-availability systems with a strict focus on idempotency, high-scale caching, and resilient data flows.
Production-ready AI-driven platforms focused on backend reliability, distributed systems, orchestration workflows, and scalable software infrastructure.
Full-stack anomaly detection platform that ingests P&L data, performs rule-based break detection, and generates AI-driven risk explanations with a human-in-the-loop review workflow (Accept/Reject + audit trail). Designed for low-latency backend processing with Redis-backed caching and replay safety.
AI-powered ETL Ops console that analyzes Airflow pipeline failures, performs structured root-cause classification, and recommends retry-safe remediation steps. Includes incident templates, exportable summaries, and mock Jira/Slack integrations to simulate real SRE workflows.
Centralized AI orchestration backend supporting multi-tenant RAG architecture, configurable grounding modes, dynamic system prompt templates, multi-API key rotation, persona-driven responses, and Telegram/Web interfaces. Built as a reusable AI service layer for future AI-Ops extensions.
Redis-backed idempotency middleware designed to guarantee atomic state transitions and exactly-once execution under retries and concurrent duplicate requests. Implements distributed locking via Lua scripts, configurable TTL, and fail-open/fail-closed operational modes.
Deep dives on distributed systems, idempotency, caching, and production backend engineering.
Mar 2025
How serving data from memory stabilized P99 latency and handled a 70% increase in peak traffic limits.
Mar 2025
Building APIs that survive network partitions, aggressive frontend retries, and duplicate concurrent requests.
Ongoing
Backend architecture, ETL pipelines, and agentic AI workflows for enterprise-scale systems.
My journey began in full-stack web development, but I quickly gravitated toward the heavy lifting: engineering the distributed backend systems, data pipelines, and intelligent workflows that make platforms scale.
Wissen Technology (Client: Morgan Stanley)
Wissen Technology (Client: Morgan Stanley)
Wissen Technology (Client: Morgan Stanley)
"Ashirvad is the kind of backend engineer you trust with your most critical systems. He picks up unfamiliar tech stacks incredibly fast, immediately contributing production-ready code. When he owns a service, I know it will be delivered robustly with zero handholding."
Have an interesting engineering challenge? Whether you're scaling a complex backend, building out data pipelines, or just want to talk system architecture, I'd love to hear from you. Drop me a message below.
Roles: Backend Engineer, Data Engineer, AI Platform EngineerLocation: Bengaluru, India — open to remote & hybridTimeline: Available for immediate start on high-impact opportunities
