Senior AI Architect – AI Platform

Position Overview

We are seeking an experienced Senior AI Architect to define and evolve enterprise AI reference architectures for Generative AI, Retrieval-Augmented Generation (RAG), and multi-agent systems. This role will establish secure, scalable, reliable, and cost-efficient architecture patterns that can be reused across multiple enterprise AI use cases.

The ideal candidate combines strong software engineering expertise with hands-on experience designing production-grade agentic AI systems. The architect will own technical direction from solution scoping and architecture design through proof-of-concept validation and production readiness, working closely with platform engineering, software delivery, AI operations, and business stakeholders.

Key Responsibilities

AI Architecture and Standardization

• Develop and maintain enterprise reference architectures, standards, and reusable patterns for RAG and agentic AI solutions.
• Define system context, data landscape, service architecture, container architecture, and AI system architecture.
• Promote consistent architecture patterns and reusable services across delivery teams.

Multi-Agent and Agentic AI Design

• Design multi-agent systems using orchestrator-based, peer-to-peer, or blackboard coordination models.
• Define agent memory, state management, inter-agent communication, and execution limits.
• Determine when agentic AI is appropriate versus deterministic workflow design.

Enterprise Platform Engineering

• Design reusable architecture components that support multiple enterprise AI use cases.
• Ensure production readiness, scalability, high availability, reliability, performance, and cost efficiency.
• Design containerized and horizontally scalable deployments, including Kubernetes-based environments and multi-tenant platforms.
• Apply relevant cloud Well-Architected principles.

Security, Governance, and Compliance

• Integrate security and governance into AI architecture from the design stage.
• Address personally identifiable information (PII), sensitive data, role-based access control (RBAC), data residency, and AI safety guardrails.
• Coordinate with AI operations and security teams to meet enterprise compliance requirements.

AI Evaluation and Observability

• Establish evaluation frameworks, golden datasets, and regression testing practices.
• Design logging, tracing, and observability across agents, services, and AI workflows.
• Define metrics to identify performance issues and diagnose production regressions.

Stakeholder Leadership and Delivery Governance

• Translate business objectives and KPIs into effective technical architecture decisions.
• Lead architecture reviews and present recommendations at formal design and validation checkpoints.
• Guide delivery teams in implementing approved reference architectures and reusable components.
• Support proof-of-concept assessments, technical debt evaluation, and production transition.

Hands-On Technical Leadership

• Apply strong software engineering principles to evaluate and challenge implementations.
• Collaborate with platform and delivery teams to validate architectural decisions.
• Maintain accountability for architecture quality from initial design through proof-of-concept validation and production support.

Required Qualifications

• Senior-level software engineering and architecture experience, with personal ownership of complex AI or GenAI systems from design through production.
• Proven hands-on experience designing and deploying production multi-agent or agentic AI systems.
• Strong understanding of agent orchestration, memory, state management, inter-agent communication, multi-tenancy, and agentic versus deterministic workflows.
• Enterprise platform engineering experience, including Kubernetes or similar container technologies, horizontal scaling, reliability, availability, performance, and cost optimization.
• Hands-on AWS experience and familiarity with Amazon Bedrock AgentCore, Strands, and Model Context Protocol (MCP).
• Experience designing RAG solutions and vector retrieval systems.
• Practical knowledge of AI security, PII protection, RBAC, data residency, governance, and enterprise guardrails.
• Ability to establish technical standards, influence engineering teams, engage business stakeholders, and independently defend architecture decisions.
• Strong hands-on software engineering skills and the ability to evaluate production implementations.

Preferred Qualifications

• Experience in energy, power generation, manufacturing, or other industrial environments.
• Master’s degree or PhD in Computer Science, Software Engineering, or a related technical discipline.
• AWS certifications, such as AWS Solutions Architect or AWS AI Practitioner.
• Experience developing enterprise reference architectures and mentoring delivery teams.