Master's in Management Studies (MMS)
Finance
Mumbai University · India
The business and financial lens behind architecture decisions.
AI Product ArchitectEnterprise AISolution Architecture
Keerthi Damaraju, AI Product Architect & Enterprise Solutions Architect. Designing the architecture behind intelligent enterprises.
Keerthi Damaraju. AI Product Architect and Solutions Architect with 12+ years of experience designing enterprise AI platforms, cloud-native systems, APIs, distributed architectures and digital transformation programs.
- Generative AI
- Agentic AI
- RAG
- AI Platforms
- Enterprise Architecture
- Cloud
- APIs
- Knowledge Graphs
Fig. 01
What makes the difference
Most architects go deep on one layer. My work sits where the layers meet: turning business strategy into AI platforms that connect models, knowledge, workflows and the systems an enterprise already runs on.
- AI
- Enterprise Architecture
- Product Thinking
- Cloud
- Integration
- Data
- Security
- Digital Transformation
01What I architect
Four areas where AI meets enterprise reality.
Each one is a discipline on its own. The value comes from designing them together, so the platform is intelligent, connected and trustworthy at the same time.
- 01
Enterprise AI Platforms
Enterprise AI ecosystems that bring LLMs, RAG, enterprise data, APIs and secure cloud services together as one platform.
- LLM & RAG architecture
- Vector search & knowledge graphs
- Cloud-native services
- 02
Agentic AI Systems
Agent workflows with reasoning, access to enterprise tools, orchestration, governance and a human in the loop where it matters.
- Agent orchestration
- Tool calling & workflows
- Human oversight
- 03
Enterprise Integration
Connecting AI platforms to enterprise applications through secure APIs, event-driven architecture, microservices and proven integration patterns.
- API architecture
- Kafka event streaming
- CRM, OMS & EHR integration
- 04
AI Governance & Security
Identity, authorization, explainability, observability and auditability built into AI platforms as architecture, not afterthoughts.
- OAuth2 / OIDC, RBAC / ABAC
- Explainability & audit
- Privacy & compliance
02AI Architecture
How I think about enterprise AI architecture.
Seven layers, each with a job. Models are only one of them. Most of the hard decisions live in knowledge, integration and trust.
Explore the architecture
Pick a capability to see where it lives in the stack, or open any layer.
RAG
Grounding models in enterprise knowledge
Architected RAG pipelines combining vector databases, semantic search and knowledge graphs so model output is grounded in enterprise context.
- Components
- LLMsRAGSemantic SearchVector DatabasesQdrantKnowledge Graphs
- Applied at
- Tulasea Inc.
Where people meet AI: web and mobile channels, copilots, conversational interfaces and the enterprise applications teams already use.
ExperienceMicrosoft Copilot adoption and conversational AI across 1000+ users at Comcast; conversational AI systems within the Tulasea platform.
Coordinates reasoning and action: agents, agentic workflows, prompts and the tool calls that let AI act on enterprise systems safely.
ExperienceWorkflow orchestration and AI-assisted workflows in the Tulasea platform; intelligent automation initiatives at Comcast.
The models and retrieval that produce answers, predictions and recommendations, grounded in enterprise knowledge.
ExperienceLLMs, RAG pipelines and semantic search at Tulasea; recommendation engines, fraud and anomaly detection and risk scoring at Comcast.
Enterprise knowledge made usable: vectors for similarity, graphs for relationships, and data platforms for scale.
ExperienceGraph-driven intelligence with JanusGraph and Qdrant at Tulasea; Databricks and Apache Spark for high-volume data at Comcast.
Connects AI to the business: APIs, event streams and microservices bridging CRM, ERP, EHR and operational systems.
ExperienceSecure API orchestration with EHR and operational systems at Tulasea; Kafka-based integration of CRM and OMS at Comcast; ERP and CRM integration earlier in her career.
Cloud-native infrastructure that keeps AI workloads scalable, resilient and repeatable to deliver.
ExperienceAWS and Azure at Tulasea; Kubernetes, Docker and CI/CD for enterprise AI workloads at Comcast.
Identity, access, oversight and accountability applied across every layer above, not bolted on at the end.
ExperienceLed AI governance architecture at Tulasea: authentication, authorization, explainability, observability, auditability, privacy and compliance.
Requests flow down, context and responses flow back up. Trust and governance apply to every layer.
03Approach
From business problem to AI platform.
AI is one part of the delivery. The platform only works when the business case, the architecture, the controls and the operating model are designed together.
Understand
Start from the business, not the model.
- Business goals
- Users
- Processes
- Enterprise constraints
Architect
Shape the target state across every layer.
- Solution architecture
- AI architecture
- Data architecture
- Integration architecture
Design for Trust
Build governance into the design.
- Security
- Governance
- Privacy
- Explainability
Engineer for Scale
Make it buildable and resilient.
- APIs
- Microservices
- Cloud
- Data
- Infrastructure
Operationalize
Run it, measure it, improve it.
- Monitoring
- Observability
- CI/CD
- Audit
- Optimization
04Featured work
Platforms and programs, told as architecture.
Five case studies across enterprise AI, real-time intelligence and digital transformation. Proprietary details stay out. The architecture thinking stays in.
- Case study 01Tulasea Inc.
AI Product Architect · May 2024 – Present
Enterprise Generative AI Platform
Enterprise-grade AI platform architecture combining LLMs, RAG pipelines, vector databases, semantic search, knowledge graphs, conversational AI and workflow orchestration for healthcare and enterprise decision ecosystems.
- Generative AI
- RAG
- AI Agents
- Knowledge Graph
- Enterprise Architecture
- Case study 02Comcast
AI Solutions Architect · Oct 2021 – Apr 2024
Enterprise AI Transformation
Enterprise-wide AI transformation spanning Microsoft Copilot adoption, intelligent automation, conversational AI and AI-assisted productivity across 1000+ users.
- Enterprise AI
- Microsoft Copilot
- Automation
- Conversational AI
- AI Adoption
- Case study 03Comcast
AI Solutions Architect · Oct 2021 – Apr 2024
Real-Time Intelligence Architecture
Event-driven, microservices-based architecture using Kafka, Databricks and Apache Spark for real-time analytics, fraud and anomaly detection, recommendations, risk scoring and workflow automation.
- Kafka
- Databricks
- Spark
- Microservices
- Real-Time AI
- Case study 04Al Turki Enterprises
Digital Transformation Manager · Apr 2017 – Mar 2021
Enterprise Digital Transformation
Enterprise-wide transformation of workforce management and business workflows with SAP SuccessFactors, process automation, integration and reporting, delivered across multiple business units.
- Digital Transformation
- SAP SuccessFactors
- Enterprise Integration
- Automation
- Case study 05Lighthouse Consulting
Product Manager – Enterprise Solutions · Dec 2015 – Apr 2017
ERP & CRM Enterprise Transformation
ERP and CRM transformation focused on business process optimization, workflow automation, API integration and customer-centric reporting.
- ERP
- CRM
- APIs
- Product
- Digital Transformation
Career at a glance
- Years of experience
- 12+
- Enterprise, solution and AI architecture
- Users in AI transformation
- 1,000+
- Copilot, automation and conversational AI at Comcast
- Career roles
- 5
- From systems analysis to AI product architecture
- Industry domains
- 3
- Telecom, healthcare and enterprise operations
05Experience
From systems analysis to AI product architecture.
Five roles in India, Oman, the United States and now remote. Each one added a layer: systems, product, transformation, integration, then AI.
– Present/Remote
AI Product Architect
Tulasea Inc.
Architecting enterprise AI and digital transformation platforms for intelligent healthcare and enterprise decision ecosystems.
- Designed enterprise-grade AI platform architectures that integrate LLMs, RAG pipelines, vector databases, knowledge graphs, semantic search, workflow orchestration and conversational AI.
- Designed graph-driven intelligence using JanusGraph and Qdrant for relationship-aware recommendations and contextual reasoning.
- Led architecture definition for AI governance: authentication, authorization, explainability, observability, auditability, data privacy and regulatory compliance.
- Python
- FastAPI
- Kubernetes
- Docker
- Kafka
- Databricks
- Qdrant
- JanusGraph
- +8 more
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- Built on scalable cloud-native and distributed architectures.
- Developed microservices-based architectures enabling real-time contextual intelligence, recommendation systems, operational insights and AI-assisted workflows.
- Designed secure API orchestration frameworks integrating enterprise systems, EHR platforms, operational systems, external APIs and distributed enterprise applications.
- Collaborated with product, engineering, operations, infrastructure, cybersecurity and executive stakeholders to align architecture with business transformation objectives.
- Established architecture standards, reusable integration patterns, technical documentation frameworks and enterprise design principles for scalable AI adoption.
- Evaluated emerging technologies and defined enterprise architecture roadmaps for AI platform evolution and operational optimization.
Python · FastAPI · Kubernetes · Docker · Kafka · Databricks · Qdrant · JanusGraph · AWS · Azure · OAuth2 · OIDC · REST APIs · Vector Databases · Microservices · Event-Driven Architecture
– /United States
AI Solutions Architect
Comcast
Architected enterprise AI and digital solutions across CRM, order management, operational platforms and distributed applications.
- Led enterprise-wide AI transformation initiatives spanning Microsoft Copilot adoption, intelligent automation, conversational AI, operational intelligence and AI-assisted productivity across 1000+ users.
- Designed high-volume integration workflows using Kafka event streaming, Databricks, Apache Spark, APIs and cloud-native platforms.
- Developed microservices architectures supporting real-time analytics, fraud detection, anomaly detection, recommendation engines, operational risk scoring and workflow automation.
- Databricks
- Apache Spark
- Kafka
- Kubernetes
- Docker
- Python
- SQL
- Power BI
- +6 more
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- Integrated CRM, Order Management Systems (OMS), operational platforms, APIs and distributed enterprise applications using event-driven architectures.
- Designed enterprise data ingestion and API integration frameworks connecting customer systems, operational systems, enterprise applications and analytics platforms.
- Worked with product management, engineering, infrastructure, operations, cybersecurity and vendor teams to keep delivery aligned with enterprise architecture principles.
- Contributed to architecture governance, CI/CD automation, observability, release management, deployment strategies and cloud-native scalability.
- Developed executive dashboards and operational intelligence platforms in Power BI and Tableau for decision-making and KPI monitoring.
- Supported Kubernetes and Docker-based deployments for scalable, resilient and reliable enterprise AI workloads.
Databricks · Apache Spark · Kafka · Kubernetes · Docker · Python · SQL · Power BI · Tableau · REST APIs · CI/CD · Event-Driven Architecture · Distributed Systems · Microservices
– /Sultanate of Oman
Digital Transformation Manager
Al Turki Enterprises
Led enterprise-wide digital transformation across workforce management, workflow modernization and process automation.
- Led digital transformation initiatives involving SAP SuccessFactors, workforce management systems, workflow modernization and process automation.
- Designed solution architectures, business workflows, integration models, technical specifications and transformation roadmaps.
- Coordinated integration planning, UAT, production rollout and change management across multiple business units.
- SAP SuccessFactors
- Workflow Automation
- Enterprise Integration
- Dashboards & KPIs
- UAT
- Change Management
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- Worked closely with business stakeholders, infrastructure teams, vendors and implementation partners to deliver enterprise solutions.
- Designed reporting ecosystems, executive dashboards and KPI frameworks for operational visibility and enterprise planning.
- Supported architecture alignment for enterprise security, governance, compliance and scalable operational workflows.
– /Sultanate of Oman
Product Manager – Enterprise Solutions
Lighthouse Consulting
Led ERP and CRM transformation initiatives with a focus on process optimization, automation and integration.
- Led ERP and CRM transformation initiatives involving business process optimization, workflow automation and enterprise integration strategy.
- Worked on solution design, API integration workflows, reporting systems and customer-centric digital transformation.
- Ran stakeholder workshops, BRDs, Agile delivery coordination, implementation tracking and rollout.
- ERP
- CRM
- API Integration
- Reporting
- Agile
- BRDs
View detailsHide details
- Collaborated with engineering and functional teams to define scalable technical solutions aligned with business objectives and operational requirements.
– /India
Technical Systems Analyst
Tata Consultancy Services
Designed enterprise application solutions and integration architectures for global enterprise clients.
- Designed enterprise application solutions, technical workflows and system integration architectures for global enterprise clients.
- Worked on application modernization involving distributed systems and large-scale operational platforms.
- Contributed to conversational AI innovation initiatives and AI-assisted enterprise application concepts.
- Enterprise Applications
- System Integration
- Distributed Systems
- SDLC
- Release Management
View detailsHide details
- Collaborated with development, QA, infrastructure and client teams across the full SDLC.
- Supported deployment coordination, defect analysis, production support and enterprise release activities.
06Competencies & tech stack
What an AI Solutions Architect needs to know.
Eight competency areas, from AI strategy to responsible AI, and a technology ecosystem grouped the way an architecture is: intelligence, knowledge, integration, platform, trust and delivery.
Key competencies
- 01
AI Solution Architecture
Turning business problems into AI platforms that can be built, run and trusted.
- Enterprise AI platform architecture
- AI architecture roadmaps
- Emerging technology evaluation
- AI strategy & use-case prioritization
- AI business cases & ROI
- Build vs. buy and model selection
- Reference architectures
- Cost, latency & token optimization
- AI evaluation strategy
- 02
Generative & Agentic AI
LLM applications that are grounded, tool-capable and safe.
- LLM & RAG architectures
- AI agents & agentic workflows
- Conversational AI & copilots
- Workflow orchestration
- Prompt & context engineering
- Multi-agent orchestration
- Tool calling & MCP integration
- Fine-tuning & model adaptation
- Guardrails & output validation
- 03
Data, Knowledge & ML
The data and models that make AI useful in context.
- Knowledge graphs & semantic search
- Recommendation engines
- Predictive analytics
- Fraud & anomaly detection
- Real-time data ingestion
- Embedding & retrieval design
- MLOps & model lifecycle
- Model monitoring & drift
- Data quality for AI
- 04
Enterprise & Integration Architecture
Connecting AI to the systems a business already runs on.
- Solution & enterprise architecture
- API architecture
- Event-driven & microservices architecture
- Enterprise integration patterns
- CRM, OMS, ERP & EHR integration
- Domain-driven design
- Legacy modernization
- API product & lifecycle management
- 05
Cloud & Platform Engineering
Scalable, resilient foundations for AI workloads.
- Cloud-native architecture (AWS, Azure)
- Kubernetes & Docker
- CI/CD automation
- High availability & scalability
- Observability
- Infrastructure as code
- GitOps
- Multi-cloud & hybrid design
- SRE practices
- 06
Security, Governance & Responsible AI
Identity, privacy and accountability designed in from the start.
- AI governance
- Authentication & authorization (OAuth2, OIDC, RBAC, ABAC)
- Explainability & auditability
- Data privacy & regulatory compliance
- Zero trust architecture
- Prompt injection & LLM threat modeling
- AI risk frameworks (NIST AI RMF, EU AI Act)
- 07
Leadership & Delivery
Aligning people, programs and architecture decisions.
- Executive & cross-functional stakeholder alignment
- Architecture standards & governance
- Digital transformation programs
- Change management & rollout
- Vendor & partner collaboration
- Agile delivery
- Architecture review boards
- Architecture decision records
- Team mentoring & enablement
- 08
Industry Domains
Where the work has been applied.
- Telecom
- Healthcare
- Enterprise operations
- Workforce management
- Customer experience ecosystems
Technology ecosystem
Generative & Agentic AI
19Designing LLM applications, retrieval and agent behavior.
- Generative AI
- LLMs
- RAG
- AI Agents
- Agentic AI
- Conversational AI
- AI Copilots
- Explainable AI
- Prompt Engineering
- Context Engineering
- Multi-Agent Systems
- Tool / Function Calling
- Model Context Protocol (MCP)
- Embeddings
- Hybrid Search & Reranking
- Structured Outputs
- Fine-tuning (LoRA / PEFT)
- Guardrails
- LLM Evaluation
Foundation Models & AI Platforms
10Model providers and managed AI platforms.
- Microsoft Copilot
- Azure OpenAI Service
- Amazon Bedrock
- Google Vertex AI
- Anthropic Claude
- OpenAI GPT
- Google Gemini
- Meta Llama
- Mistral
- Hugging Face
AI Frameworks & Orchestration
08Building blocks for retrieval pipelines and agent workflows.
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- DSPy
- Haystack
Machine Learning & MLOps
15Predictive models and the pipelines that keep them healthy.
- Predictive Analytics
- Recommendation Systems
- Fraud Detection
- Anomaly Detection
- Risk Scoring
- scikit-learn
- PyTorch
- TensorFlow
- XGBoost
- MLflow
- Kubeflow
- Amazon SageMaker
- Azure Machine Learning
- Feature Stores
- Model Monitoring & Drift
LLMOps & Observability
10Tracing, evaluating and monitoring AI systems in production.
- Observability
- Audit Logging
- LangSmith
- Langfuse
- Arize Phoenix
- RAGAS
- OpenTelemetry
- Prometheus
- Grafana
- Datadog
Vector, Search & Knowledge
13Where enterprise knowledge is indexed, related and retrieved.
- Vector Databases
- Qdrant
- Knowledge Graphs
- JanusGraph
- Semantic Search
- Pinecone
- Weaviate
- Milvus
- pgvector
- Chroma
- Elasticsearch
- OpenSearch
- Neo4j
Data Engineering & Analytics
16Pipelines, storage and analytics at enterprise scale.
- Databricks
- Apache Spark
- Airflow
- SQL
- Cassandra
- DuckDB
- MinIO
- Power BI
- Tableau
- Delta Lake
- dbt
- Snowflake
- Apache Flink
- PostgreSQL
- MongoDB
- Redis
Architecture & Integration
15How AI connects to the rest of the enterprise.
- Microservices
- Distributed Systems
- Event-Driven Architecture
- Kafka
- REST APIs
- FastAPI
- Python
- Enterprise Integration Patterns
- GraphQL
- gRPC
- Webhooks
- API Gateways (Kong, Apigee, Azure APIM)
- Service Mesh (Istio)
- CQRS & Event Sourcing
- Domain-Driven Design
Cloud & DevOps
11Cloud-native foundations for running AI workloads reliably.
- AWS
- Azure
- Kubernetes
- Docker
- GitLab CI/CD
- Google Cloud
- Terraform
- Helm
- GitHub Actions
- Argo CD
- Serverless (Lambda, Azure Functions)
Security & Responsible AI
18Identity, access, privacy and accountability for AI platforms.
- OAuth2
- OIDC
- RBAC
- ABAC
- IAM
- API Security
- AI Governance
- Secure Enterprise Integrations
- Zero Trust
- Microsoft Entra ID
- HashiCorp Vault
- PII Detection & Redaction
- OWASP Top 10 for LLMs
- NIST AI RMF
- EU AI Act
- ISO/IEC 42001
- HIPAA
- GDPR
Architecture Practice
09Frameworks and artifacts for designing and governing systems.
- Architecture Standards & Documentation
- Architecture Roadmaps
- Technical Governance
- TOGAF
- C4 Model
- ArchiMate
- Architecture Decision Records
- AWS Well-Architected
- Azure Well-Architected
Product & Delivery
11Delivering, measuring and communicating the work.
- React.js
- JIRA
- Confluence
- Agile Delivery
- BRDs
- UAT
- Next.js
- TypeScript
- Scrum
- Figma
- Lucidchart
Architecture disciplines
- Enterprise Architecture
- Solution Architecture
- Integration Architecture
- API Architecture
- Cloud-Native Architecture
- Event-Driven Architecture
- Technical Governance
- Architecture Roadmaps
- High Availability & Scalability
- Platform Engineering
- Omnichannel Digital Platforms
07Architecture principles
Six principles behind every design.
The decisions change from platform to platform. These stay the same.
- 01
Business-Aligned
Architecture starts from enterprise strategy and the outcome the business needs. Technology choices follow.
- 02
Secure by Design
Identity, authorization and data protection are part of the first design, not a review at the end.
- 03
API First
Capabilities are exposed through well-defined, secured APIs so AI can reach enterprise systems and systems can reach AI.
- 04
AI With Governance
Explainability, observability and auditability make AI decisions traceable and accountable.
- 05
Cloud Native
Containerized microservices and automated delivery keep platforms portable, resilient and repeatable.
- 06
Designed for Scale
Event-driven, distributed patterns let platforms grow in users, data and use cases without redesign.
AI Product Architect
Keerthi Damaraju[ADD PROFILE PHOTO]
08About
I work at the intersection of AI, enterprise architecture, product thinking and digital transformation.
Enterprise AI becomes valuable when models, knowledge, workflows and business systems work as one platform. That is the part I architect: LLMs, RAG pipelines, vector search and knowledge graphs, connected through secure APIs and event-driven integration to the CRM, order management, EHR and operational systems a business depends on.
My path into AI architecture ran through the enterprise itself. I started as a systems analyst at Tata Consultancy Services, moved into product management for ERP and CRM transformation, led digital transformation programs in Oman, then architected enterprise AI and integration platforms at Comcast before taking on AI product architecture at Tulasea.
That history shapes how I work. I translate business and technical requirements into architecture that can actually be built, secured and operated at enterprise scale, with governance, identity, observability and auditability designed in from the start rather than added at the end.
Most of the job is alignment. I work day to day with product, engineering, operations, UX/UI, infrastructure, cybersecurity, vendors and executive leadership, so the architecture reflects what the business needs and what the teams can deliver.
What I connect
- Business strategy
- Product requirements
- AI
- Enterprise architecture
- Engineering execution
Who I work with
Product · Engineering · Operations · UX/UI · Infrastructure · Cybersecurity · Vendors · Executive leadership
Education
Bachelor of Engineering (BE)
Electronics & Telecommunication
Mumbai University · India
The engineering foundation for systems and distributed architecture.
Contact
Let's architect what comes next.
Interested in enterprise AI platforms, Generative AI architecture, agentic systems or digital transformation? I'd be glad to hear what you are building.
- Phone
- +971 54 491 5339