AI Solution Architect / Analyst

Quick Facts

• Role: AI Solution Architect / Analyst

• Focus: Architect, implement, and validate AI systems in production; technical lead for a smaller team

Description

You will design AI system architectures from high-level concepts to detailed component diagrams, working hands-on with LLM, RAG pipelines, and agent orchestration frameworks. The role includes memory architectures for agents, governance and security for agent systems, enterprise integrations, and observability/tracing for LLM pipelines. You will validate ideas through PoCs before production and provide technical leadership for a smaller development team.

Responsibilities

• Design AI system architecture (high-level to component-level diagrams)

• Build and work with LLM, RAG pipelines, and agent orchestration frameworks

• Propose multi-layer memory architectures for agents (vector databases, graph databases, in-memory, hybrid approaches)

• Define governance for agent systems (policy engines, deterministic decision layers, pre-flight gates, consent/authorization pipelines)

• Design integrations with enterprise systems (real-time APIs, data warehouses, message/event brokers, event-driven architecture)

• Work on observability for LLM pipelines (metrics, evaluation, production feedback)

• Containerize and deploy to cloud platforms using Docker and Kubernetes/OpenShift on Azure

• Lead a smaller development team technically (technical direction, prioritization, splitting work, support on complex topics)

• Validate hypotheses via PoCs before moving to production

Requirements

• Deep technical depth in AI/ML: LLM, RAG, vector search, agent architectures, multi-agent orchestration

• Ability to design distributed system architectures (sync/async communication, event-driven patterns, API design)

• Practical experience with LLM orchestration frameworks (LangChain, LangGraph, or equivalent)

• Practical cloud experience ideally with Azure, including network security, access policies, and sandbox environments

• Experience with containerization and orchestration: Docker and Kubernetes/OpenShift

• Ability to read and create architectural diagrams (UML, component, and sequence diagrams)

• Systematic thinking and understanding of impact across the stack (infrastructure to application layer)

• Knowledge of LLM governance and security: PII handling, consent management, RBAC/ABAC, audit logs

• Ability to technically lead a smaller development team and take responsibility for technical quality

• Communication skills to argue technically with developers, architects, and the customer

Benefits

• Work on large enterprise projects in energy and banking, with potential future scope in finance, healthcare, or public sector

• Opportunity to influence architecture and also contribute to implementation (not a purely theoretical role)

• Build and validate AI agents with real production impact; AI is treated as a practical tool, not a buzzword