Sandeep Midde

Technology & Delivery Leader

Modernizing the complex, highly regulated platforms that cannot afford to fail.

Two decades leading technology and delivery for mission-critical finance platforms, from enterprise ERP programs for global clients to governing a $2.5T+ AUM financial ecosystem in regulated banking.

The focus stays constant. Align architecture, budgets, and cross-functional teams to protect uptime and cut total cost of ownership, and now bring the same rigor to hands-on, evaluation-backed GenAI product architecture.

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~20 yrs
Enterprise technology & delivery
$2.5T+
AUM finance platform governed
99.8%
Availability, up from 99.2%
Portrait of Sandeep Midde

About

Close to the architecture. Accountable for the outcome.

A career spent where a system going down is a regulatory event, not an inconvenience. That means owning architecture, budget, and roadmap for finance-critical platforms, and turning technical depth into outcomes that auditors, executives, and engineers can all verify.

VP
Leadership level
Senior leadership over finance-critical platforms
3
AI products shipped
Designed and built end to end since 2026
190+
Countries
Adoption of a global talent platform
900+
Objects retrofitted
In one PeopleSoft Finance upgrade, with zero critical defects

Applied AI

GenAI, built with a banker's discipline.

Three AI products designed and shipped end to end, each written up in full.

Krishna Uvaca cover

RAG · Conversational AI · In production

Krishna Uvaca

A RAG platform on the Bhagavad Gita that gives grounded answers, cites the verse each one comes from, and remembers the conversation.

88% accuracy across 500 test conversations

Read the case study
Observer Prime cover

Agentic workflow automation

ObserverPrime

Multi-stage content pipelines with provider-agnostic LLMs, background jobs, and resumable execution. A human approves every step.

Automated test coverage for production-grade reliability

Read the case study
Project Elara cover

AI knowledge platform

Elara

Turns unstructured material into reusable knowledge through automated ingestion, extraction, and retrieval. Think of it as a personal AI tutor.

Led AI strategy & architecture

Read the case study
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Impact

Outcomes that moved the business.

Six outcomes, and the numbers behind them.

Modernization strategy

Cut projected TCO by 70% with one business case

Led the build-versus-buy decision to replace a legacy ERP platform. Built the roadmap, the cost model and a lean delivery team, then won Finance CTO sponsorship for a cloud-hosted platform developed in house.

  • $4M → $1.2MProjected TCO
  • CTOSponsorship secured

Platform modernization

Turned hours of finance processing into minutes

Replaced a third-party intermediary with a direct Bloomberg integration, added a Python computation layer fed by Kafka, and automated a daily reconciliation across 142 models that used to be a manual task.

  • 99%Faster rate updates
  • 3h → 2mWorkflow time
  • 5h → 20mDaily reconciliation

Reliability and resilience

Kept critical platforms running at 99.8%

Built a dedicated L2 support model with runbooks, SLAs, clear ownership and escalation paths, cutting MTTR by 30 to 60%. Turned disaster recovery into a rehearsed routine, with failover coordinated across 8 teams twice a year.

  • 99.8%Availability, up from 99.2%
  • 3 to 5hVerified recovery

Governance and compliance

Made the compliant path the default path

Designed COSO-aligned data retention that enforces Fed and bank-mandated retention windows, with file-receipt validation and access controls. Led the move of 52 downstream feeds to governed Apigee APIs ahead of an enterprise-wide mandate.

  • 8 yrsAuditable data trail
  • 52Feeds moving to Apigee

Organization and delivery

Doubled release speed by redesigning the team

Restructured one team stretched across four modules into pods with their own backlogs and release rhythm. Owned hiring and performance, mentored developers and architects, and stood up a cloud-modernization team on a standardized CI/CD framework.

  • 2×Release frequency
  • 20%Fewer deployment incidents

Applied AI

Tested GenAI before trusting it in production

Directed a retrieval-first RAG feasibility study with an evaluation harness and no-answer detection, giving leadership a clear go or no-go decision. Then shipped Krishna Uvaca, a governed RAG platform now live in production.

  • ~80%Source-grounded answers
  • 88%Accuracy, Krishna Uvaca

Principles

Three principles, earned the hard way.

Evaluate before committing

The cheapest mistake is the one measured early. Cost models and evaluation harnesses come before budget is locked.

Proven by $4M → $1.2M TCO case · RAG go/no-go

Compliance is architecture

Audit-readiness bolted on at the end is brittle. Build classification, validation, and access control in from the start, so the compliant path is the default.

Proven by COSO-aligned retention · 52 feeds moving to Apigee

Reliability is engineered

Checkpoint restart, resilient reprocessing, runbooks, and rehearsed failover. Failure paths are designed, not improvised.

Proven by 99.2% → 99.8% availability · 3 to 5h DR

Career

Two decades of
compounding depth.

From PeopleSoft Finance consultant to VP of a $2.5T+ AUM finance platform to independent AI builder, each chapter built the judgment for the next.

Jul 2026 to Present
Independent
AI / ML product development

AI Technical Product Development

Designing and shipping GenAI products, agentic workflows, and low-code apps from idea to deployment.

GenAIRAGAgentic AI
Key highlights (3)
  • Architected and built Krishna Uvaca, an end-to-end RAG conversational AI platform with context-aware retrieval, grounded generation, and multi-turn memory, reaching 88% accuracy over 500 test conversations
  • Designed and delivered ObserverPrime, an agentic workflow system orchestrating multi-stage content pipelines with provider-agnostic LLMs, background jobs, resumable execution, and automated tests
  • Led AI strategy and architecture for Elara, a knowledge platform turning unstructured data into reusable knowledge through automated ingestion, extraction, and retrieval
Oct 2012 to Jun 2026
Wells Fargo India
~14 years

Vice President

Technical strategy and platform governance for a $2.5T+ AUM finance ecosystem in a regulated banking environment.

Platform GovernanceModernizationRisk & ComplianceTeam Leadership
Key highlights (9)
  • Set architectural guardrails and target-state direction across finance-critical programs
  • Directed a retrieval-first RAG feasibility study with an evaluation harness and no-answer detection, reaching ~80% source-grounded responses and informing a production go/no-go
  • Built a Python computation layer over PeopleSoft with Kafka consumption, turning a 3-hour async workflow into under 2 minutes
  • Integrated Bloomberg BEAP, retiring a third-party intermediary and cutting response time 99% on reusable architecture
  • Streamlined GL allocation feeding P&L with checkpoint restart and adaptable rule skipping, making it 58% faster (3.5h → 1.5h) across 2M+ rows a day
  • Standardized L2 support, cutting MTTR by 30 to 60% and lifting availability from 99.2% to 99.8%
  • Restructured into module-aligned pods and standardized CI/CD for cloud readiness, moving releases from monthly to biweekly with 20% fewer deployment incidents
  • Led a data-driven build-vs-buy strategy that secured CTO sponsorship and cut projected TCO from $4M to $1.2M
  • Scaled DR from annual to every 6 months across 8 teams, with 3 to 5 hour execution, and architected COSO-aligned auditable data retention
Aug 2011 to Sep 2012
Oracle India
PeopleSoft consulting

Staff Consultant

PeopleSoft consulting on a talent platform adopted across 190+ countries.

RequirementsLocalization
Key highlights (2)
  • Drove requirements discovery with global stakeholders on the PeopleSoft Talent Acquisition model, delivering a ~20% performance improvement and stabilized workflows
  • Delivered bilingual and multilingual support for Inspira (UN talent acquisition), enabling adoption across 190+ countries
Jul 2006 to Jul 2011
Infosys
Charles Schwab · Dow Jones

Technology Architect

PeopleSoft Finance delivery for Charles Schwab and Dow Jones under CMMI Level 5 governance.

ERP DeliveryCMMI Level 5Client Leadership
Key highlights (5)
  • Managed cross-functional delivery for the Charles Schwab account across development, support, design, and upgrade engagements
  • Led a PeopleSoft Finance upgrade retrofitting 900+ custom objects across P2P and O2C (GL, AR, AP, BI, Purchasing) into stable production
  • Owned delivery governance for recurring incidents under CMMI Level 5, cutting the ticket backlog by 30 to 40% during finance close
  • Led the Dow Jones PeopleSoft re-implementation, clearing the last blocker to its phased funding release
  • Chaired the PeopleSoft LOB Communication Group, connecting 30+ teams through knowledge-sharing sessions, newsletters, and hackathons

Toolkit

What I bring to the table.

Executive Toolkit & Governance

Technical Strategy & Architecture Enterprise Architecture (TOGAF) Delivery Governance Risk & Compliance (COSO · CMMI L5 · Audit Readiness) Budget, Roadmap & TCO Ownership Build-vs-Buy Strategy Executive & Stakeholder Management Agile / SAFe Delivery Talent Development & Hiring Responsible AI & AI Governance

Hands-on engineering capabilities

Enterprise platforms & integration

PeopleSoft · Oracle · Salesforce REST / Apigee Kafka Event-Driven / SOA Python Playwright Git · Jenkins · Harness · uDeploy · Jira Azure

Applied AI engineering

RAG Agentic Workflows Prompt Engineering Guardrails & Evaluation LangChain ChromaDB Multi-Model Architecture

Credentials

Learning, continuously.

Certifications

  • Project Management Professional (PMP) PMI
  • AI Security & Governance Securiti
  • Agentblazer Champion 2026 Salesforce
  • Enterprise Product Management Fundamentals Microsoft
  • Azure Fundamentals (AZ-900) Microsoft
  • Certified PeopleSoft Technical Consultant Oracle

Education

  • Post Graduate, AI & Machine Learning IIIT Bangalore
  • MBA, IT & Financial Accounting ICFAI
  • B. Pharmacy (Hons.) BITS Pilani

Contact

Let's talk about what's next.

Modernizing a critical platform, putting governance around AI, or building a technology and delivery leadership team? I'd welcome the conversation.

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