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Arun Yadav, Principal AI Systems EngineerAvailable in India and globally, IST

ArunYadav

From operational problem to working system.

I turn ambiguous business operations into AI systems with clear boundaries and observable behavior.

Position

Selected work across AI platforms, products, and reliability.

I map customer workflows, build the backend and intelligence layer, connect enterprise dependencies, and make runtime behavior observable.

Experience10+ years
Current focusAgents as infrastructure
Strongest edgeAI, backend, operations
Target rolesPrincipal engineering roles
40%
lower resolution time

Reported enterprise delivery outcome for a banking helpdesk.

30%
lower cloud cost

Reported enterprise delivery outcome from cloud migration and architecture optimization.

25%
lower latency

Reported enterprise delivery outcome from retrieval and caching improvements.

60%
less manual workflow effort

Reported enterprise delivery outcome from workflow automation.

Selected work

Six projects.
One way of working.

Each case covers the operating problem, the constraints, the choices, and the result.

AI SaaS ProductCreators and agenciesLive product

Agentic Content OS

PillarQuill

An agentic content operating system that turns research into strategy, governed creation, publishing and learning loops.

Open case study
RESEARCHSTRATEGYCREATEPUBLISH CONTENT LOOP
My roleFounder + end-to-end architecture
SystemResearch-to-publish agent workflow
Hard partWorkflow continuity across channels
StatusLive studio
AI-Native Vertical SaaSCandidates and institutionsActive product

Inception to Offer

Placement OS

A closed-loop career system from role discovery and diagnostics to practice, mentoring, CV iteration, interviews and offer tracking.

Open case study
DISCOVERBUILDPRACTICEPROVEOFFER FEEDBACK LOOP
My roleProduct + platform architecture
SystemGo modular monolith + AI workflows
Hard partOne stateful journey across many roles
EvidenceHTMX · Casbin · OTel
AI Reliability / EvaluationR&DProduct architecture

Reliability Intelligence

Agent Trace

A reliability layer that compresses raw agent traces into recurring failure shapes, release gates and actionable root-cause evidence.

Open case study
CLUSTERFAILURE SHAPES RAW TRACES → SIGNAL → RCA → RELEASE GATE
My roleProduct + reliability architecture
SystemTrace scanners + clustering
Hard partSemantic failures with valid HTTP 200s
EvidenceEvals · RCA · quality gates
Capabilities

Skills shown through the work where they were applied.

Each capability maps to a delivery problem, technical choice, and area of ownership.

01

Forward Deployed Engineering

Field Operations · Hive · ServiceXPro
Discovery, workflow mapping, integrations, deployment and reusable primitives

+
02

Agent Orchestration

Hive · Field Operations · Banking · PillarQuill
Routing, tools, memory, structured artifacts and human escalation

+
03

Retrieval & Knowledge Systems

Field Operations · Banking · Grant Copilot · Company Brain
Hybrid RAG, permissions, citations, graphs and ingestion pipelines

+
04

Backend & Platform Engineering

Hive · Placement OS · ServiceXPro · SaaS Baseline
APIs, modular boundaries, queues, caching, identity and data systems

+
05

Multi-Tenancy & Enterprise Security

Hive · Placement OS · PillarQuill · ISO Platform
RBAC, tenant isolation, credentials, auditability and policy enforcement

+
06

AI Reliability & Evaluation

Trace Intelligence · Hive · Field Operations
Traces, semantic SLOs, eval gates, fallbacks, RCA and incident readiness

+
07

Cloud & Delivery

Hive · Field Operations · Banking
AWS, containers, Kubernetes, IaC, CI/CD and environment governance

+
08

Product Architecture

PillarQuill · Placement OS · ServiceXPro
Problem framing, domain models, closed loops and end-to-end execution

+

Additional systems

A broader body of work across SaaS, compliance, evaluation, business automation and domain-heavy backend platforms.

07

ServiceXPro

AI CRM and operations platform for service businesses

Live product
08

Closed-Loop Company Brain

Organizational memory → reasoning → role-authorized action

Architecture / prototype
09

ISO Compliance Platform

Multi-standard workflows across ISO 27001, 20000, 9001, 13485 and 22000

Product architecture
10

Human-in-the-Loop Evaluation SaaS

Task givers, evaluators, bidding, KYC and quality-control workflows

Product architecture
11

Operational SaaS Systems

Salon, appointments, billing, inventory, real estate and PostGIS workflows

Backend systems
12

Grant Application Copilot

Document intelligence, hybrid RAG, citation validation and governed assistance

Enterprise AI prototype
13

Creator Studio

AI-assisted content creation, publishing, analytics and compliance workflows

AI product
14

Reusable SaaS Platform Baseline

Auth, RBAC, subscriptions, queues, caching, audit, quotas and analytics

Reference architecture
Experience

Backend foundations.
Enterprise AI delivery.

Current

BMW TechWorks

Enterprise agent platform, AI orchestration, FastAPI, AWS/Pulumi, Kubernetes, IAM and observability.

Senior Engineer / AI Platform
Previously

Ascendion

Principal-level backend and GenAI delivery across enterprise systems, secure RAG and integration architecture.

Principal Engineer
Previously

Accenture

Python platforms, APIs, automation, CI/CD, cloud delivery and engineering leadership.

Senior Software Engineer / Team Lead
Foundation

Syntel

Enterprise backend services, databases, automation and production delivery discipline.

Software Engineer

I work across business logic, model behavior, and production operations.

Profile

How I work

I reduce ambiguity first. Then I define boundaries, failure modes, ownership, and operating signals before scaling an implementation. I remain hands-on through APIs, orchestration, data models, infrastructure, and production diagnostics.

BuildPython · FastAPI · Go · HTMX
IntelligenceLangGraph · RAG · tools · memory
DataPostgres · pgvector · Redis · Neo4j
OperateAWS · Kubernetes · OTel · Langfuse
GovernRBAC · IAM · tenancy · audit
OptimizeLatency · cost · caching · reliability
ContactFor AI platforms, applied AI products, and difficult delivery constraints.

Working on an AI system that has to hold up in production?

© 2026 Arun Yadavarun3q@gmail.com