Raghunath Manyam
Principal Engineer · Systems Architect · AI-Augmented Cloud Modernization
Currently leading an 18-engineer reinsurance modernization at a Tier-1 carrier — migrating a $B PL/I + DB2-LUW ledger to AWS — and sole architect of a Claude-based agentic COBOL→TypeScript factory now in internal production use.
The bridge most engineers don't have
AI architecture leading, cloud-native delivery underneath, mainframe depth as the moat.
Career Journey
20+ years building mission-critical systems at Cognizant
Lead Engineer
Cognizant — Financial Services Client
Enterprise Reinsurance modernization at a Tier-1 carrier — a 25+ year-old PL/I + DB2-LUW system carrying $billions in active treaty exposure, with ~50 daily actuarial and finance consumers and no write-freeze tolerance. Own technical direction for 18 engineers across data, application, and reporting tracks. Designed the metadata-driven migration pattern that collapsed the 23-table mainframe → Aurora migration into 3 Glue jobs (vs. ~69 the obvious design called for); architected the Cash Clearance latency win with DynamoDB caching over partitioned Aurora reads. Cut Cash Clearance search latency from 3–5 minutes to <5 seconds on 100K+ row queries; shipped the event-driven reporting layer feeding ~50 treaty and finance consumers. Also sole architect and builder of the Modernization Factory — an agentic pipeline (built as an MCP server) that turns JCL + COBOL into AWS Lambda, Step Functions, and Aurora DDL with a parity test suite. Its 8 stages route per-stage across Claude tiers — Haiku for the triage gate, Sonnet for structured extraction and codegen, Opus with extended thinking for the one architectural-routing decision and the parity-test design — roughly 70–80% cheaper than running Opus throughout. Human-in-the-loop by default: the pipeline pauses for schema review before it writes any code. In internal production use, output shipping after minor human review.
- Owned every architecture decision across 18 engineers — set the bar on AWS, Aurora, and IAM patterns the org hadn't yet standardized
- Designed the metadata-driven pattern that collapsed a 23-table migration into 3 Glue jobs — fewer surfaces to test, version, and operate
- Architected the DynamoDB-over-Aurora caching layer that held the 3–5 min → <5s win under concurrent load on 100K+ rows
Project Manager — Operations
Cognizant — Financial Services Client
L1–L3 application operations across a 150-system production portfolio under ITIL delivery — every reactive ticket triage came out of the same team's hours. Owned operational outcomes for 14 direct and ~50 indirect reports. Stood up the team's first proactive monitoring + alerting layer so every shift ran off shared dashboards; drove the focus from triage volume to root-cause elimination — identifying repeat incidents and shipping permanent fixes rather than re-running the same ticket loop. Improved portfolio stability by 60% over three years.
- Stood up the first proactive monitoring + alerting layer — every shift ran off the same dashboards instead of reactive ticket triage
- Drove the practice shift from triage volume to root-cause elimination — the change that compounded into the 60% stability gain
- Owned outcomes for 14 direct + ~50 indirect reports across an ITIL-aligned ops org spanning 150 production applications
Sr Software Engineer
Cognizant — Financial Services Client
Major rating-engine re-platform at a US personal-lines carrier — the actuarial business owned the rules but couldn't change them without an engineering round-trip, because they lived as 100+ hardcoded PL/I procedures over IMS-DB lookups. Owned the business-logic analysis track. Translated 100+ rating algorithms into Ratabase semantics — each validated against an actuarial SME; ran re-rate cycles across full state policy books when rating errors surfaced; pair-designed the Informatica + PowerExchange data pipelines with the Principal architect. Individual re-rate cycles returned several-hundred-thousand-dollar customer refunds; the pipeline became the data backbone for actuarial pricing and underwriting decisioning.
- Translated 100+ PL/I + IMS-DB rating rules into Ratabase — every algorithm validated against a business SME, every round-trip removed
- Ran state-level re-rate cycles when rating errors surfaced — individual corrections returned several-hundred-thousand-dollar refunds to customers
- Pair-designed the Informatica + PowerExchange pipelines that became the data backbone for actuarial pricing and underwriting decisioning
Software Engineer
Cognizant — Financial Services Client
Entry into regulated-industry tech — production support and feature work on P&C insurance applications. Owned DB2 SQL performance tuning on claims workloads, requirements analysis with business analysts, and quality audits across the application portfolio. Built the mainframe-adjacent data, release-discipline, and customer-money instincts every later role compounded on.
B.E. in Electronics & Communication Engineering
Sri Krishnadevaraya University
Engineering Journal
Structured technical documentation on enterprise patterns, cloud architecture, and data engineering.
Where the LLM goes in regulated reinsurance: a reference architecture for the API boundary and the audit trail
Every LLM tutorial assumes you can send the data to the API. In regulated reinsurance that assumption breaks before the first prompt — most of the architecture lives in the boundary, not the model.
Vector DB vs. structured store vs. live API: where each piece of a serverless RAG actually belongs
Most serverless RAG architectures fail not at the model but at the data layer — by trying to make the vector DB carry weight it was never designed for. The interesting decision is which piece of context belongs where, and the question that decides it is freshness.
Designing the Modernization Factory: an 8-stage agentic pipeline for COBOL→AWS, and where each Claude model earns its place
Turning mainframe COBOL into AWS-native code with an LLM is easy to demo and hard to make repeatable. The architecture that makes it a factory rather than a party trick is in the pipeline shape, the per-stage model routing, and the one checkpoint that sits before the irreversible work.
Grounding a career site in a vector DB: a $10/month chatbot that has to tell the truth
Building /chat on my own site forced three decisions I usually get to defer on someone else's product: where the cost ceiling lives, what counts as a citation, and how to keep an embedding pipeline from breaking a deploy.
Mainframe modernization without a re-platform freeze: a pattern from five years of rating-engine work
Every mainframe modernization plan dies on the same rock: the business can't tolerate a write freeze, and the engineers can't tolerate live dual-writes against a system they don't fully understand. The way out isn't faster cutover. It's separating the rules from the engine.
Three minutes to five seconds: where the cache belongs in a serverless reinsurance app
The query was correct. The database was sized. The bug was that the cost lived in the wrong layer — and the fix was a cache that became load-bearing for correctness, not just speed.