Solution Architect [AI/ML, LLM, AWS]

We are seeking a highly experienced Solution Architect to design, guide, and govern scalable software solutions across the organization—ranging from individual components to fully integrated enterprise platforms. This role requires strong expertise in AWS cloud technologies, AI infrastructure, and advanced AI governance practices, ensuring solutions align with business strategy, security standards, and responsible AI policies.

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

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