Technical Architect

The Technical Architect is responsible for the overall technical direction of complex AI-enabled products and platforms. You will work across teams and client workstreams, making sure that application, data, integration, cloud, security, and AI decisions fit together. This is not a role that sits outside delivery. You will guide Tech Leads, review the most important design choices, and step into difficult technical areas when the team needs experienced judgement.

• Lead technical discovery and define the end-to-end architecture for complex products and platforms

• Turn business goals and non-functional requirements into clear decisions across applications, data, integration, identity, security, AI, deployment, monitoring, resilience, and support

• Define reusable patterns, reference architectures, and practical engineering guardrails for project teams

• Lead technology choices and explain the trade-offs across security, scalability, maintainability, cost, delivery speed, portability, and vendor dependency

• Design enterprise patterns for RAG, agents, orchestration, evaluation, model routing, memory, LLMOps, and responsible AI

• Review critical solution designs, architecture decisions, security considerations, and production-readiness plans

• Guide Tech Leads, resolve cross-team design issues, and work directly on high-risk or technically difficult areas when required

• Identify architectural risks early and agree practical mitigation and fallback options

• Lead technical discussions with senior client, business, security, data, and technology stakeholders

• Support proposals, estimates, implementation roadmaps, and technical due diligence for new opportunities

• Improve consistency and reuse across projects without imposing unnecessary process

Experience

• A bachelor's or master's degree in computer science or a related field, or equivalent practical experience

• 9 to 12 years of software engineering experience, including ownership of architecture for complex production systems

• Experience guiding multiple engineering teams, systems, or major workstreams

• Deep knowledge of distributed systems, APIs, integration patterns, cloud architecture, security, data platforms, and modern application development

• Strong understanding of enterprise AI architecture, including RAG, agentic systems, evaluation, observability, privacy, governance, and operational controls

• A track record of making and explaining architectural trade-offs to both engineers and senior stakeholders

Technical Depth

• Azure and AWS architecture across compute, containers, serverless, networking, identity, security, data, AI, monitoring, and cost management

• Microservices and modular monoliths, event-driven architecture, queues, caching, API management, and distributed-system reliability

• Identity and access management, secrets and key management, network controls, threat modelling, auditability, privacy, and software supply-chain security

• AI platform patterns including model gateways, provider abstraction, vector search, content ingestion, evaluation pipelines, tracing, guardrails, and human oversight

• Platform engineering practices including CI/CD, infrastructure as code, environment strategy, service-level objectives, disaster recovery, performance engineering, and FinOps

• Ability to produce architecture diagrams, decision records, standards, reference implementations, and production-readiness criteria that teams can use