Manish Prajapati_
Manish Prajapati · Senior IC Engineer

I build AI systems the way I build backend systems — for production, not for demos.

Applying ~9 years of enterprise distributed systems rigor to agentic AI workflows, zero-fabrication retrieval architectures, and resilient event-driven microservices. Designing software for real failure modes, partition tolerance, and strict data integrity — not disposable tech demos.

Manish Prajapati, Senior IC Engineer
RoleSenior Full Stack / Backend IC
FocusDistributed Systems & AI
LocationGurgaon · Remote Global
StatusAvailable for Senior Remote Roles
Distributed Systems

~9 Years

Enterprise backend engineering at Amdocs (July 2017 – Present)

Subscribers Powered

Millions

CRM SaaS platform for Japan's tier-1 telecom operator (J:COM)

Latency Reduction

40%

CSR query handling time reduction via workflow optimization

Engineers Mentored

50+

Structured technical onboarding, architecture reviews & code quality

01 · Enterprise Scale & Distributed Systems

Production Systems at Scale

Designing, optimizing, and operating mission-critical distributed backend architectures for Japan’s tier-1 telecom CRM platform (J:COM) at Amdocs, powering millions of active subscribers.

Pillar 01Apache Kafka

High-Throughput Event Streaming

Apache Kafka · Distributed Consumers

The Problem: Handling millions of asynchronous billing and network event transactions across telecom accounts without message loss, head-of-line blocking, or latency spikes.

Engineering: Designed partitioned consumer topologies with strict key-hashing for partition-affinity, idempotent record processors, and dead-letter queue (DLQ) replay mechanics.

Guaranteed at-least-once delivery with end-to-end deduplication
Pillar 02Spring Boot

CQRS & Transactional Outbox

Spring Boot · PostgreSQL // CDC

The Problem: Maintaining relational consistency across independent service boundaries during high-concurrency order placement and account provisioning.

Engineering: Decoupled write and read models via Transactional Outbox pattern and change data capture (CDC), preventing distributed 2PC bottlenecks and ensuring immediate local commit durability.

Zero dual-write inconsistency across distributed state machines
Pillar 03Circuit Breakers

Resilience & Graceful Degradation

Circuit Breakers · Backpressure Propagation

The Problem: Downstream partner system brownouts and legacy telecom interface latency cascading into core transaction processing and blocking ingest pipelines.

Engineering: Engineered bounded thread pools with backpressure propagation, dynamic circuit breakers, and rate-limiting fallbacks that preserve essential operations under 10x traffic surges.

Graceful degradation preserving core transactional pathways
Enterprise AI & Automation Track

Real-Time Operator Copilot & Workflow Automations

40% Query Handling Time Reduction
CRM AI Call Center Operator Copilot (POC)

Engineered an enterprise proof-of-concept combining Azure Speech-to-Text streaming with an LLM semantic layer. Ingested live customer service conversations in real time, auto-surfacing subscriber history, relevant policy documentation, and recommended resolution pathways to human operators without manual catalog navigation.

Agentic Workflow Orchestration & Vector Retrieval

Designed intelligent workflow automations using n8n, webhooks, LLM APIs, and Pinecone vector indexing to enable natural-language task execution across internal enterprise tools. Streamlined CSR repetitive operational cycles, directly contributing to the verified 40% reduction in query handling time.

Interactive Architecture Trace: Smriti Request Lifecycle

The following 7 steps illustrate the verified request lifecycle through the zero-fabrication conversational architecture.

  1. Step 1: Message Received — Client Ingestion & Token Auth: Incoming query validated against Firestore auth token before retrieval or generation logic runs. (Verified in source: app/api/chat/route.ts)
  2. Step 2: Query Embedded — Vector Representation: Embedded with gemini-embedding-2-preview at request time into a 768-dim float array. (Verified in source: app/api/chat/route.ts)
  3. Step 3: Memory Retrieval — In-Process Cosine Similarity: Application-level similarity over memory array; top 5 selected. Keyword fallback if embedding missing. (Verified in source: app/api/chat/route.ts)
  4. Step 4: Tone & Script Detected — Deterministic Rule Classifier: Not embedding-based. Regex Unicode script detection and keyword emotion matcher (e.g. "yaad aati" -> Grieving). (Verified in source: lib/language-analysis.ts)
  5. Step 5: Grounded + Toned Prompt — Zero-Fabrication Synthesis: Injects top-5 verified memories and explicit tone directive. Model instructed never to invent memories outside vault. (Verified in source: app/api/chat/route.ts)
  6. Step 6: Generation & Cascade — Resilience Fallback Cascade: Primary gemini-3.1-flash-lite fails over through 3.6-flash, 3.7-flash, and flash-latest with 600ms backoff. (Verified in source: lib/gemini.ts)
  7. Step 7: Response Returned — Non-Generative Fallback Delivery: Delivered to client. Unconfirmed memory updates surfaced as Save/Review cards; zero unconfirmed Firestore writes. (Verified in source: firestore.rules)
02 · Flagship AI Engineering Case Study

Smriti — Digital Memorial Companion

Production-grade systems design applied to agentic AI: deterministic execution pipelines, zero-fabrication retrieval constraints, and verified failure cascades.

SmritiStatus: Deployed & functional, informal validation

Conversational memorial companion with zero-fabrication retrieval architecture

Technical Descriptor: "Zero-fabrication retrieval architecture" · "Structurally grounded, non-generative-fallback design"

Unlike generic RAG prototypes that patch hallucinations with prompt directives, Smriti closes fabrication structurally at the storage and retrieval boundary.

Interactive Execution Topology

Verified branch fork at Node 1, asymmetric tone latching at Node 4, and 600ms resilience backoff at Node 6.

7-Node Verified Lifecycle
1. Message Received2. Query Embedded3. Memory Retrieval4. Tone & Script Detected5. Grounded + Toned Prompt6. Generation & Cascade7. Response Returned
In-Process Vector Retrieval

Bounded Memory & Cosine Similarity

Computes top-5 cosine similarity over memory embeddings directly in-process (zero network round-trip). Bounded vault design eliminates distributed vector database latency overhead.

Deterministic Emotional Gating

Rule-Based Tone Classifier

Regex-driven tone categorization evaluates synchronously in-process (zero LLM overhead), bypassing model classification nondeterminism and guaranteeing grief-appropriate conversational warm grounding.

Memory Write Gating

Zero Unconfirmed Firestore Writes

All memory updates require explicit client UI confirmation (Save / Review / Dismiss). Prevents cross-turn hallucination accumulation from entering the permanent database.

Resilience & Failover Cascades

Deterministic 503 Exponential Backoff

600ms backoff interval on upstream model unavailability, automatically rerouting to fallback models without dropping conversational state or breaking SSE stream buffers.

Honest Engineering Tradeoff: In-process retrieval is bounded to hundreds of memories per vault; order-of-magnitude scaling would require indexing refactoring.
Read 11-Section Architectural Breakdown→
03 · Selected Systems & Engineering Blueprints

Systems Index

Curated mobile architectures, client prototypes, and technical specifications with transparent, verified status labels.

Inkleaf

Native Android Prototype // Active Research

Native Android Technical Document Reader

SYS 01
The Engineering Problem

Reading dense 5,000-block technical architecture documents and markdown diagrams on mobile without UI thread stutter or out-of-memory crashes.

Architectural Decisions & Implementation
  • ▸Clean 3-layer architecture (Data / Domain / UI) in Kotlin and Jetpack Compose.
  • ▸Streaming I/O via Android Storage Access Framework (SAF) using DigestInputStream and SHA-256 content fingerprinting.
  • ▸Decoupled diagram viewing: Inline placeholder cards navigating to an isolated vector canvas screen (DiagramViewerScreen.kt).
  • ▸Headless Mermaid SVG rendering cached by content hash and decoded via Coil SvgDecoder.
Status Transparency & Tradeoff:

Prototype stage: active engineering focus on large-document AST chunking and complex diagram first-load rendering. Not published on Google Play.

KotlinJetpack ComposeSAF StreamingHeadless MermaidCoil SVGMarkwon

Khopcha Discovery

Architecture Blueprint // PRD & TAD v1 Signed Off

Hyperlocal Food Discovery Platform Spec

SYS 02
The Engineering Problem

Discovering authentic regional street food dishes and artisanal vendors in Ranchi without forcing non-tech-savvy vendors onto complex delivery apps.

Architectural Decisions & Implementation
  • ▸PostGIS spatial indexing (ST_DWithin, ST_DistanceSphere) via pure SQL compiled through Drizzle ORM with zero query-engine overhead.
  • ▸Zero-vendor-friction crowdsourcing: Vendors mapped via Field Scout crowd submissions and AI scrapers rather than vendor login portals.
  • ▸Leaflet / OpenStreetMap abstraction behind interchangeable MapView interfaces, avoiding early map billing exposure.
  • ▸Offline-tolerant Progressive Web App (PWA) with background sync service workers.
Status Transparency & Tradeoff:

Full product and technical architecture specification (PRD & TAD v1 completed Sep 8, 2026). Strictly an architecture showcase, not a shipped commercial product.

PostGIS / PostgreSQLNode.jsDrizzle ORMLeaflet / OSMPWA Spec
04 · Technical Craft & Disciplines

Technical Craft

No badge walls or arbitrary skill percentage bars. Software craftsmanship defined by architectural principles, engineering tradeoffs, and verified production capabilities.

Discipline 01
AI & Retrieval Systems

AI & Retrieval Systems

Zero-fabrication architecture, deterministic pipeline design, and resilience routing.

Core Architectural Principles:

  • ▸In-process cosine vector retrieval over bounded memory vaults (<2ms execution, zero DB lock-in)
  • ▸Deterministic synchronous regex classifiers (in-process, zero LLM overhead or hallucination)
  • ▸Explicit UI memory-write gating with user action cards (zero unconfirmed state writes)
  • ▸Multi-model failover cascades with exponential backoff handlers across transient upstream errors
Google Gemini APIsVector EmbeddingsFirestoren8n AutomationsPrompt Engineering
Discipline 02
Distributed Systems & Backend

Distributed Systems & Backend

High-throughput transactional systems designed for resilience and partition tolerance.

Core Architectural Principles:

  • ▸Partitioned event streaming with consumer group affinity and key-hashing
  • ▸Transactional Outbox pattern preventing dual-write inconsistency across microservice boundaries
  • ▸Idempotent event processing and Dead Letter Queue automated replay mechanics
  • ▸Strict schema evolution, CDC replication, and backward compatibility standards
JavaC++Spring BootApache KafkaPostgreSQLDocker
Discipline 03
Frontend Architecture & Motion

Frontend Architecture & Motion

Performant, accessible interfaces that communicate system state without gimmicks.

Core Architectural Principles:

  • ▸Fixed-ratio aspect containers guaranteeing sub-0.01 layout stability (CLS < 0.01)
  • ▸Strict reduced-motion parity with instant static topological schemas for accessibility
  • ▸DOM/SVG stateful signal graph rendering over compositor-only properties (transform, opacity)
  • ▸Full keyboard operability and WCAG 2.2 AA compliance (with AAA contrast on core content)
TypeScriptReact 19Next.js App RouterTailwind CSS 4GSAP / SVG
Discipline 04
Reliability & Engineering Rigor

Reliability & Engineering Rigor

Production-readiness measured by how software behaves when dependencies fail.

Core Architectural Principles:

  • ▸Explicit tradeoff documentation for all architectural decisions (naming compromises honestly)
  • ▸Telemetry-driven error boundaries, circuit breaking, and graceful degradation
  • ▸Zero database bloat in static and client-side distribution bundles
  • ▸Rigorous automated type-safety, contract testing, and contract verification
CI/CD PipelinesStructured LoggingOpenTelemetryStatic Export / Zero Bloat
05 · Engineering Leadership & Writing

Mentorship & Architecture Notes

Engineering leadership demonstrated through structured technical mentorship, code quality advocacy, and published deep-dive architecture analyses.

Technical Leadership & Developer Mentorship

Amdocs · Engineering Enablement & Culture

25% Faster Team Onboarding

Mentored 50+ software engineers through structured technical onboarding, hands-on architectural design sessions, and comprehensive code reviews. Championed automated testing, clean service boundaries, and rigorous documentation standards, resulting in a verified 25% acceleration in new-hire time-to-productivity and sustained improvements in multi-team code maintainability.

Verified Publications & Architectural Guides
Microsoft Tech Community // Security & Protocols2022

Resolved AuthenticationFailure problem on IMAP and POP3 protocols using Client Credential flow for OAuth2.0

Authored a technical solution addressing OAuth2-based authentication failures in enterprise systems, enabling secure headless migration from legacy basic auth to client credential token flows.

dev.to // Distributed ArchitectureAug 2024

CQRS: The Design Pattern That's Changing the Game (and How You Can Use It Too)

Practical architectural guide to balancing load between read and write models, optimizing independent scalability, and decoupling domain operations from query projections.

dev.to // AI Architecture & RetrospectiveSep 2024

Smriti: What If You Could Talk to Them, Just One More Time?

Architectural deep dive into why generic RAG fails memorial requirements, and how in-process vector retrieval and regex emotional classifiers eliminate fabrication structurally.

06 · Selected Client Platforms & Family Ventures

Selected Builds & Ventures

Targeted production tools, utilities, and client platforms engineered with clean design and pragmatic performance.

Developer Utility

dslr-to-webp

High-efficiency bulk image transcoding utility optimizing high-resolution DSLR RAW/JPEG photography assets into lightweight WebP streams via Node.js streams and libvips.

Node.jsSharp / libvipsStreams API
Production Web App

hotel-shasha-website

Boutique hospitality booking and regional discovery portal in Jibhi, Himachal Pradesh, built with static-first Next.js rendering, Cloudinary optimization, and edge caching.

Next.jsTypeScriptTailwind CSSCloudinary
Family Business Venture

laxmi-flour-mill

Market research, product line planning, and FSSAI regulatory compliance for a regional multigrain flour venture, focusing on scale-up strategy beyond hyperlocal retail distribution.

Market ResearchFSSAI ComplianceProduct Strategy
Production Web App

shiv-tour-and-travels

Regional transport and vehicle booking platform deployed on GitHub Pages with clean responsive layout and direct WhatsApp inquiry coordination.

HTML/JSResponsive CSSGitHub Pages
Available for Senior Remote IC Roles

Let's Build Production Systems.

I work with engineering teams, tech leads, and founders building distributed backend architectures, resilient microservices, and verified production AI systems.

Gurgaon · Remote Global (IST / Cross-Timezone Alignment) · High technical ownership & asynchronous rigor.