Architecture & Solution Design
Design end-to-end architectures spanning:
Component-level services (microservices, APIs)
Enterprise-wide ecosystems
Define architecture patterns, standards, and reusable frameworks
Translate business requirements into scalable and secure technical solutions
Ensure interoperability across systems, data layers, AI services, and platforms
Enterprise Architecture Strategy
Develop and maintain enterprise architecture roadmaps
Align IT strategy with business goals and digital transformation initiatives
Establish governance models (TOGAF/SAFe or similar)
Lead architecture review boards and technical decision-making processes
Architect and optimize cloud-native and hybrid solutions using AWS services
Define cloud migration strategies and modernization approaches
Ensure high availability, resiliency, cost optimization, and performance
Implement Infrastructure-as-Code and automation best practices
AI, Data & Intelligent Systems Architecture
Design AI/ML infrastructure, pipelines, and enterprise integration patterns
Architect solutions incorporating LLMs, generative AI, and intelligent agents
Guide adoption of AI technologies within enterprise platforms and products
RAG (Retrieval-Augmented Generation)
Feature stores and data pipelines
Model deployment, versioning, and scaling
AI Governance, Observability & Control
Define and implement enterprise AI governance frameworks covering:
Responsible AI usage (fairness, bias mitigation, explainability)
Data privacy, lineage, and compliance
AI risk classification and policy enforcement
Establish AI observability and monitoring capabilities, including:
End-to-end tracing of AI/ML and LLM flows using tools such as OpenTelemetry
Monitoring of prompts, responses, latency, and model behavior using platforms like Langfuse or equivalent
Metrics for model performance, drift, hallucination rates, and usage patterns
Design and enforce agent governance and control mechanisms, including:
Monitoring and auditing of autonomous and semi-autonomous AI agents
Guardrails for agent behavior, tool usage, and decision boundaries
Human-in-the-loop (HITL) workflows and escalation patterns
Policy-based control over agent actions and integrations
Implement AI lifecycle governance, including:
Model validation, approval workflows, and audit trails
Continuous evaluation and feedback loops
Secure model and prompt management
Cross-Disciplinary Architecture Leadership
Act as a strategic liaison across Semantic, Data, and ML architecture domains
Facilitate alignment between knowledge graphs, ontologies, data platforms, and ML systems
Provide architectural guidance to specialized architects, ensuring cohesive enterprise integration
Bridge gaps between business semantics, data engineering, and machine learning pipelines
Security, Compliance & Governance
Ensure architectures meet enterprise security standards (e.g., Zero Trust)
Define policies for data governance, access control, and auditability
Align AI and cloud solutions with regulatory and compliance frameworks
Collaboration & Leadership
Work with engineering, product, data, and AI teams to align solutions
Mentor architects and senior engineers
Act as a trusted advisor to leadership and stakeholders
