Solution Architect

Architecture & Solution Design

Design end-to-end architectures spanning:

Component-level services (microservices, APIs)

Domain platforms

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

Cloud Architecture (AWS)

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

Establish patterns for:

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