NM·MATLA
NAGAMALLAIAH MATLA · "NAGA" · 22+ YEARS IN SOFTWARE ENGINEERING & TECHNOLOGY LEADERSHIP

Engineering
Leadership for
The AI Era

I own engineering end to end — product, platform, data, ML, and agentic AI — for multi-tenant enterprise SaaS serving 300+ enterprise customers across 100+ countries. Personalization and decisioning systems shipped a decade before GenAI. Today: multi-agent AI in production, not in demos.

PLATFORM50B+ events/yr·99.99% uptime·sub-second at 1000× growth
AILangGraph·Bedrock AgentCore·LangSmith·MCP·RAG·Eval Gates
SECURITYSOC 2 / GDPR operated·DevSecOps·−60% vulnerabilities
LEADERSHIP12+ yrs leading EMs & senior teams·SAFe RTE·global time zones
Get in touch See the AI practice
50B+
Events / Year
99.99%
Uptime
300+
Enterprise Customers
100+
Countries Served
02 · Profile

One accountable owner —
strategy to production.

I'm an engineering leader who owns end-to-end engineering organizations for AI-driven, multi-tenant SaaS platforms: product engineering, backend services and APIs, frontend, the data platform (ingestion, ETL/ELT, streaming and batch, quality, governance, observability), the ML platform (deployment, monitoring, retraining), and cloud infrastructure. One owner, accountable for all of it.

I've been shipping intelligence into production since long before it was fashionable — I authored a production customer-scoring algorithm in 2013 that powered marketing-efficiency programs across 17 international markets, and designed a real-time decisioning engine executing trigger-based campaigns with measured uplift. Today's agentic AI is the same discipline with better models.

My current platform runs at 50B+ events per year, 99.99% uptime, sub-second serving — with agentic AI capabilities in production. I lead a large engineering organization through Engineering Managers, distributed across India, Europe, and global time zones. And I still build: the best engineering leaders never stop.

At a Glance

  • Director of Engineering — AI-driven multi-tenant enterprise SaaS
  • 22+ years engineering · 12+ years leading managers & senior teams
  • Agentic AI in production: LangGraph, Bedrock AgentCore, LangSmith, MCP
  • Data platform end-to-end: Kafka, Spark, Databricks, Airflow, lakehouse
  • Cloud-agnostic: AWS depth, Azure production, GCP working, on-prem K8s
  • Security: SOC 2 / GDPR operated, DevSecOps, secure-design certified
  • SAFe RTE — delivery across 27 globally distributed streams
  • M.Tech IT, JNTU Hyderabad — University 7th rank, 98 GATE percentile
03 · AI Engineering

Agentic AI, operated as a discipline.

Most AI work stops at the prototype. Mine runs in production with evaluation gates, tracing, guardrails, and a deployment story that works on any cloud — or none.

Orchestration

Multi-Agent Systems

  • LangGraph — supervisor / specialist patterns
  • Amazon Bedrock AgentCore: Runtime, Gateway, Memory, Identity
  • Typed tool contracts & agent lifecycle management
  • Human-in-the-loop authorization for consequential actions
Knowledge & Tools

RAG + MCP

  • Bedrock Knowledge Bases & vector stores (S3 Vectors, pgvector, OpenSearch)
  • Hybrid retrieval, semantic chunking, source attribution
  • FastMCP — designed & built MCP servers exposing enterprise systems
  • Least-privilege tool scopes per agent
Quality & Observability

Evals, Tracing, AIOps

  • LangSmith tracing & evaluation suites
  • Golden sets, LLM-as-judge, regression evals in CI
  • Drift, hallucination, grounding & cost monitoring
  • Bedrock Guardrails + AgentCore Observability (CloudWatch)
Model Layer

Provider-Agnostic by Design

  • AWS Bedrock (Claude) & SageMaker — production depth
  • Azure OpenAI & Azure ML — production
  • Vertex AI / Gemini — working knowledge
  • Self-hosted vLLM on Kubernetes for air-gapped estates
ML Platform

Classic ML, Still Running

  • Deployment with evaluation gates; drift & performance monitoring
  • Retraining workflows; feature-engineering pipelines in production
  • Anomaly detection & predictive analytics (AI/ML Product Owner at Nokia)
  • Computer vision shipped: pose-estimation virtual try-on
Responsible AI

Governance, Not Vibes

  • Responsible-AI governance operated under SOC 2 / GDPR
  • Guardrails on inputs and outputs; audit trails end to end
  • Policy enforcement at the API boundary
  • Research-to-production translation through structured gates
04 · AI-Driven SDLC

Spec-driven development,
AI at every stage.

I run engineering organizations where AI is a governed capability across the lifecycle — not a browser tab. The spec is the contract; agents and humans both build against it, and nothing ships without passing the gates.

SPEC requirements as executable contract acceptance criteria PLAN AI-assisted design architecture review task decomposition BUILD Claude Code · Copilot agents code to spec humans own decisions VERIFY AI + human review tests · SAST · SBOM policy-as-code GATE LLM eval suite golden sets · judge blocks regressions SHIP Harness · ArgoCD GitOps · canary auto-rollback OBSERVE — LangSmith traces · SLOs & error budgets · drift / cost / grounding · incident learnings production telemetry feeds the next spec — the loop never opens AI-DRIVEN SDLC · SPEC IS THE CONTRACT · GATES ARE NON-NEGOTIABLE GOVERNED ADOPTION: COPILOT + CLAUDE CODE ROLLED OUT WITH USAGE GOVERNANCE AND ROI METRICS — VELOCITY WITH ACCOUNTABILITY
The lifecycle I operate — every stage AI-augmented, every release evaluation-gated
Spec-Driven

The spec is the source of truth

Requirements written as executable contracts with acceptance criteria. AI agents and engineers build against the same spec; drift between intent and implementation is caught by the gate, not by customers.

Governed Adoption

AI tooling with ROI metrics

GitHub Copilot and Claude Code adopted organization-wide with governance: usage policy, quality guardrails, and measured ROI — so leadership knows what AI velocity actually costs and returns.

Delivery Numbers

Velocity you can audit

Delivery predictability +35%, release efficiency +18% at portfolio scale; −60% security vulnerabilities through automation; −25% cloud cost while absorbing 1000× growth. Speed, quality, and cost balanced explicitly.

05 · Security & Trust

Security engineered in,
audited continuously.

Enterprise customers don't buy features from platforms they can't trust. I run security as an engineering discipline — certified in secure design, operated under real compliance regimes, measured by real reductions.

DEFENSE IN DEPTH — EVERY LAYER OWNED IDENTITY IAM / RBAC SSO · MFA · Cognito least privilege HITL authorization APPLICATION secure design (Purple Belt) secure coding (Orange Belt) AI guardrails I/O rate limiting · WAF DATA KMS encryption tenant isolation data governance · lineage backup & DR PIPELINE DevSecOps · SAST/DAST policy-as-code · SBOM secrets management signed artifacts ASSURANCE SOC 2 · GDPR operated CloudTrail · audit logs incident mgmt · RCA blameless postmortems MEASURED OUTCOME: −60% SECURITY VULNERABILITIES IN 8 MONTHS VIA ZERO-TOUCH AUTOMATION · NOKIA SECURITY PURPLE & ORANGE BELT CERTIFIED
Security as engineering, not paperwork
Compliance Operated

SOC 2 · GDPR in production

Not "compliance-aware" — operated. Multi-tenant isolation by design, audit trails end to end, and responsible-AI governance layered on top for LLM workloads.

Shift-Left, Automated

−60% vulnerabilities in 8 months

Led a zero-touch automation transformation embedding security scanning, policy-as-code, and SBOM generation into every pipeline — vulnerabilities down 60%, delivery efficiency up 30%, simultaneously.

AI Attack Surface

Securing agentic systems

Guardrails on model I/O, prompt-injection defenses, least-privilege MCP tool scopes, and human-in-the-loop gates on consequential agent actions — because agents are a new attack surface.

06 · Experience

Two decades of shipping.

Enterprise SaaS, consumer scale, financial platforms, mission-critical distributed systems — one career, every scale.

Aug 2023 — Present
Director of Engineering — Product Engineering
SUBEX LIMITED · BENGALURU
  • Own engineering strategy, architecture, execution, and delivery for a multi-tenant, AI-driven enterprise SaaS serving 300+ enterprise customers across 100+ countries — 50B+ events/year, 99.99% uptime, sub-second serving.
  • Run the data platform end to end — Kafka streaming, Spark/Databricks batch, Airflow orchestration, lakehouse architecture, data quality, governance, and observability.
  • Operate the ML and agentic layer — deployment with evaluation gates, drift monitoring, retraining workflows, and multi-agent AI (LangGraph, Bedrock AgentCore) with responsible-AI governance.
  • Drive engineering excellence — CI/CD with automated testing, DevSecOps, SOC 2/GDPR, and −25% cloud cost while absorbing 1000× growth.
  • Build the organization through Engineering Managers and senior engineers, with hiring, mentoring, and succession; honored with Ninjas of the Year, the company's highest honor.
AI SaaS PlatformData + ML PlatformAgentic AISLO OperationsOrg Leadership
Oct 2022 — Aug 2023
Head of Software Engineering
TECNOTREE CONVERGENCE
  • Directed full-stack engineering — backend, frontend, data, QA, DevOps — across enterprise SaaS product lines for international clients, partnering with European-HQ leadership.
  • Led the Zero Touch automation transformation: −60% security vulnerabilities and +30% quality and delivery efficiency in 8 months.
  • Built hiring frameworks, mentorship tracks, and delivery playbooks — scaling capability without proportional cost.
Full-Stack OrgDevSecOps TransformationGlobal Delivery
May 2021 — Oct 2022
Director of Software Engineering
IAAA TECHNOLOGIES · CITIZENCHAT, MIRRA, PCAH
  • Hired to build the engineering organization and cloud platform from scratch — scaled 10 → 10,000 TPS at 25% lower infrastructure cost while the flagship product grew 0 → 2M users at a 4.8/5 rating.
  • Shipped production applied AI — computer vision (pose-estimation virtual try-on) and AI content-analysis pipelines — plus a healthcare-domain product.
  • Earned the Times of India Group Best Innovation Platform Award and ET Excellence Award, with national coverage.
0→1 Platform2M+ UsersApplied AI / CVFounding Team
Jun 2015 — May 2021
Senior Engineering Manager · R&D System Architect
NOKIA SOLUTIONS & NETWORKS
  • Advanced from R&D System Architect to Senior Engineering Manager within 8 months — architecture direction for a fault-tolerant distributed platform through its cloud-native evolution.
  • Introduced SAFe company-wide; Release Train Engineer for 3 years across 27 globally distributed delivery streams — +35% delivery efficiency, +18% release efficiency.
  • Formed a new department end to end and served as AI & ML Product Owner — anomaly detection and predictive analytics delivered into the platform.
  • Security Purple Belt (Secure Design) and Orange Belt (Secure Coding); Ideation Icon Award with recognition from the then-CEO.
SAFe RTE27 StreamsAI/ML Product OwnerSecurity Belts
Jun 2011 — Jun 2015
Senior System Architect · Senior Engineering Manager
MAHINDRA COMVIVA
  • Architected high-throughput payment processing, mobile-money, and loyalty platforms with financial reconciliation and integrity controls across 17 international markets under strict SLAs.
  • Authored the production RFM segmentation/scoring algorithm and designed the Winback real-time decisioning engine — rules + behavioural ML executing autonomous, trigger-based campaigns with measured uplift.
  • Client-account SPOC advising Finance and Operations executives; TITANS Best Talent of the Year 2014 (top 10 of 3,000).
Payments / FinServAlgorithm Author17 MarketsDecisioning ML
Jun 2002 — Jun 2011
Engineering Foundations
COGNIZANT · IBM GLOBAL SERVICES · ANALOG DEVICES · PAULUS
  • Java SME at Cognizant on a critical US enterprise program; IBM Global Services — 'Project of the Year 2008' contributor.
  • Analog Devices — test-and-measurement automation cutting per-board evaluation from 2h 23m to 21 minutes.
  • Nine formative years building the depth in Java, databases, and distributed systems everything since has stood on.
Enterprise JavaAutomationDistributed Systems
07 · Platforms & Projects

The work, in full detail.

Systems I lead, architect, or built end to end — each running in production, not on slides.

Enterprise AI SaaS · Current

AI-Driven Multi-Tenant Analytics Platform

300+ ENTERPRISES
100+ COUNTRIES
50B+ EVENTS/YR · 99.99%

A cloud-agnostic, multi-tenant enterprise SaaS for analytics, applied ML, and agentic AI over high-volume event data — deployable on AWS, Azure, GCP, or private cloud. I own product engineering across its full lifecycle: architecture, data platform, ML platform, reliability, and delivery.

Platform anatomy
  • Streaming (Flink-based ETL) + batch (Spark) ingestion at 50B+ events/year
  • Lakehouse core with Druid sub-second OLAP, PostgreSQL, Redis
  • AI/advanced-analytics services and case-management workflows
  • Prometheus + Grafana observability; SLOs with error budgets
  • Kubernetes-portable deployment across all major clouds and on-prem
Technology Stack
KubernetesKafkaFlinkSpark DruidLakehousePostgreSQLRedis JavaPythonAI/ML Services Prometheus · GrafanaMulti-Cloud
Agentic AI · Award-Winning · Idea → Funded Production

Leadership Co-Pilot

SHARK TANK
INNOVATION AWARD
CLOUD OR ON-PREM

A production-grade multi-agent system for real-time performance intelligence — conceived, pitched, funded, and shipped. An orchestrator plans and routes across specialist agents (performance, quality, work-activity, collaboration, insight & coaching), grounded by RAG over historical knowledge and connected to live enterprise systems through MCP. Full architecture in section 08.

Production engineering
  • LangGraph orchestration on Bedrock AgentCore Runtime — supervisor/specialist patterns
  • AgentCore Gateway + MCP tools: Jira, GitHub, databases, documents — least-privilege scopes
  • Bedrock Knowledge Bases + vector store RAG with semantic chunking & attribution
  • AgentCore Memory, Guardrails, and Observability (CloudWatch) wired in from day one
  • LangSmith tracing and evaluation suites; golden sets and LLM-as-judge in CI
  • Cognito/IAM RBAC, KMS encryption, CloudTrail audit — enterprise-ready posture
  • Provider-portable: Bedrock (Claude) today; Azure OpenAI, Vertex, or on-prem vLLM by configuration
Technology Stack
LangGraphBedrock AgentCoreRuntime · Gateway · Memory LangSmithMCP / FastMCPFastAPIPython Knowledge BasesS3 Vectors / pgvectorGuardrails Cognito · IAM · KMSStreamlitAzure OpenAIvLLM (on-prem)
Decisioning · A Decade Before GenAI

Winback Decisioning Engine + RFM Algorithm

17 INTL MARKETS
MEASURED UPLIFT
IN PRODUCTION SINCE 2013

Personalization and decisioning shipped before it was fashionable: I authored the production RFM (Recency-Frequency-Monetary) segmentation and scoring algorithm powering marketing-efficiency programs across 17 international markets, and designed Winback — a real-time decisioning engine combining rules with behavioural ML to execute autonomous, trigger-based campaigns with measured uplift.

Why it still matters
  • Candidate selection, ranking, and trigger-based action — recommendation-class engineering
  • Directly connected to revenue: campaign uplift measured, not assumed
  • Today's agentic decisioning is the same discipline with better models
Technology Story
Algorithm DesignBehavioural MLRules Engine Real-Time DecisioningHigh-Throughput JavaRevenue-Linked
Consumer Scale · 0→1 · Award-Winning

CitizenChat & Applied AI Products

0 → 2M USERS · 4.8/5
10 → 10,000 TPS
−25% INFRA COST

As hired Director of Software Engineering, built the engineering organization and AWS cloud platform from scratch for a consumer communication product — scaling throughput 1000× while cutting infrastructure cost 25% — and shipped production computer vision (pose-estimation virtual try-on) and AI content-analysis pipelines, plus a healthcare product.

Built from zero
  • Founding engineering team hired, structured, and scaled
  • Real-time messaging platform through hypergrowth
  • CV in production: pose estimation for virtual try-on
  • Times of India Best Innovation Platform + ET Excellence Award
Technology Stack
AWSReal-time MessagingComputer Vision Mobile-FirstAuto-ScalingCost Engineering
Nonprofit · Pro Bono · Sole Technical Owner

Nationwide Operations Platform (NGO)

NATIONWIDE
DB · API · WEB · ANDROID
MISSION-CRITICAL

Technical lead and sole maintainer for a national nonprofit's membership and operations system spanning Oracle Autonomous Database, ORDS REST APIs, a Java web application, and a native Android app — real production ownership including incident response, data-integrity safeguards, surgical restores, and annual multi-layer rollovers. Separately, took over a vendor-built events platform and established full GitHub Actions CI/CD from zero.

Ownership in practice
  • Mass-corruption recovery with root-cause fixes (compound triggers, integrity constraints)
  • Backup-before-write and additive/reversible change discipline
  • Full CI/CD established on inherited code; ops runbooks authored
Technology Stack
Oracle ADBPL/SQLORDS REST Java · TomcatAndroidGitHub Actions Node.js · ReactPostgreSQL
08 · Architecture

Agentic AI, deploy-anywhere.

My reference architecture for production agentic systems. Orchestration, knowledge, safety, and operations are constant; the model provider and infrastructure are pluggable — the same system runs on AWS, Azure, GCP, or a fully air-gapped on-prem Kubernetes cluster.

CLIENTS API LAYER ORCHESTRATION · AGENTCORE RUNTIME KNOWLEDGE & TOOLS MODEL PROVIDERS · PLUGGABLE DELIVERY & OPERATIONS Web / Chat UI Slack / Integrations API Consumers API Gateway — Cognito SSO · IAM/RBAC · rate limits · input guardrails policy enforcement at the boundary · KMS · CloudTrail audit LangGraph Orchestrator on Bedrock AgentCore Runtime Orchestrator plan · route Specialist Agents domain workers Tool Agents act via MCP Critic verify + HITL AgentCore Memory · Identity · session isolation Knowledge Bases · Vector RAG S3 Vectors · pgvector · OpenSearch semantic chunking · attribution AgentCore Gateway · MCP Tools Jira · GitHub · DBs · docs · CI/CD typed contracts · least privilege AWS Bedrock Claude · managed Azure OpenAI managed Vertex AI managed vLLM · On-Prem open-weight · air-gapped GitOps Delivery Harness · ArgoCD · Helm LangSmith + Eval Gate traces · golden sets · CI block Guardrails + SLOs observability · canary · rollback SWAP FREELY
Reference architecture — constant orchestration & safety layers, pluggable model providers

Why this shape matters

The provider layer is a seam, not a foundation. Agents talk to models through one abstraction, so moving between Bedrock, Azure OpenAI, Vertex AI, or an on-prem vLLM cluster serving open-weight models is a configuration decision, not a rewrite. For banks, healthcare, and sovereign-cloud enterprises, that's the difference between a demo and a deployable system: the vector store, guardrails, and evaluation gate all run equally well inside the customer's own data center.

Safety is structural, not bolted on. Guardrails at the API boundary, a critic agent with human-in-the-loop escalation, LangSmith traces on every run, and an evaluation gate in CI that blocks any change degrading quality — before it reaches a canary, let alone production.

09 · Skills Matrix

Depth across three layers.

Leadership & Delivery

Organization DesignLeading through EMs & Tech Leads; founding-team building twice; succession planning
Delivery at ScaleSAFe certified RTE (27 streams), quarterly PI cadence, +35%/+18% efficiency gains
Business ConnectionRoadmaps tied to revenue, cost (−25% cloud at 1000×), and customer outcomes
Global TeamsIndia · Europe · US stakeholders; async-first operating rhythm across time zones
Executive CommunicationBoard-level technical direction; client-account SPOC to Finance & Ops executives

AI & Agentic Systems

OrchestrationLangGraph, Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Observability)
Knowledge & ToolsRAG (Knowledge Bases, S3 Vectors, pgvector, OpenSearch), MCP/FastMCP servers
Quality & AIOpsLangSmith, golden sets, LLM-as-judge, drift/cost/grounding monitoring, eval gates in CI
ProvidersBedrock (Claude), SageMaker, Azure OpenAI/ML, Vertex AI working, vLLM on-prem
Classic MLDeployment, retraining, feature pipelines; CV (pose estimation); anomaly & predictive analytics
AI-Driven SDLCSpec-driven development; Copilot + Claude Code governed rollout with ROI metrics

Platform, Data & Security

CloudAWS (EKS, Lambda, MSK, Kinesis, Bedrock, SageMaker), Azure (AKS, OpenAI, Databricks), GCP working; Terraform IaC
Data & StreamingKafka (10,000+ TPS), Spark, Databricks, Airflow, Flink, Druid, lakehouse/medallion
Delivery InfraHarness, ArgoCD/GitOps, Jenkins, GitHub Actions, Helm, canary + auto-rollback
SecurityDevSecOps, policy-as-code, SBOM, SOC 2/GDPR, KMS, IAM/RBAC, secure-design certified
ReliabilitySLOs/error budgets, P90 latency governance, Prometheus/Grafana/OpenTelemetry, blameless RCA
EngineeringJava/Spring Boot, Python (expert), TypeScript/Node.js, SQL; REST/gRPC/GraphQL; React, Android
10 · Recognition

Recognized at every stop.

2024 · SUBEX

Ninjas of the Year

The company's highest honor — for engineering leadership and platform impact.

SUBEX

Shark Tank Innovation Award

Leadership Co-Pilot: from idea to funded production product.

2021 · CITIZENCHAT

ET Excellence Award + TOI Best Innovation Platform

National recognition for a product that grew 0 → 2M users.

NOKIA

Ideation Icon Award

Recognized by senior leadership including the then-CEO.

2014 · COMVIVA

TITANS Best Talent of the Year

Top 10 of 3,000 — plus Credo Cap Award and Best Project 2012.

2008 · IBM + ACADEMICS

Project of the Year Contributor · University 7th Rank

IBM Project of the Year 2008; M.Tech University 7th rank, 98 GATE percentile.

11 · Credentials

Education & certifications.

Education

M.Tech, Information TechnologyJNTU HYDERABAD · UNIVERSITY 7TH RANK
B.Tech, Computer Science & EngineeringJNTU HYDERABAD
GATE98TH PERCENTILE

Certifications

FinTech Innovation & TransformationSINGAPORE BUSINESS SCHOOL, NUS
Leading SAFe (Release Train Engineer) · PMPSCALED AGILE & PROGRAM LEADERSHIP
Nokia Security — Purple & Orange BeltSECURE DESIGN & SECURE CODING
Step Up to LeadershipDALE CARNEGIE
TM Forum (Frameworx, SID, eTOM)ENTERPRISE ARCHITECTURE FRAMEWORKS
Sun SCJP / SCWCD · Oracle SQL · IBM UnixCORE ENGINEERING FOUNDATIONS
12 · Contact

Let's build what's next.

Open to senior engineering-leadership conversations — AI platforms, enterprise SaaS, and large-scale product engineering. Based in Bengaluru; open globally, including the Gulf region.

mallimatla@gmail.com +91 99000 59821 linkedin.com/in/mallimatla
Email me Connect on LinkedIn