AI Solution Architect

Systems Limited is looking for an AI Solution Architect who thinks in agents, reasons in models, and designs in flows. This is not a traditional architecture role retrofitted with AI — it is a ground-up position for someone who has built with large language models, wired up MCP connectors, and shipped AI-powered products in the real world.

You will own the end-to-end technical vision for AI-native solutions: from whiteboarding an agentic workflow to selecting the right model, harness, and integration layer. You will work closely with product, engineering, and business stakeholders to translate ambiguous problems into elegant, reliable AI architectures.

This role also comes with rare exposure: you will have the opportunity to work on AI engagements with some of System's most iconic North American customers — organizations where the work you design will operate at real scale and leave a lasting mark.

Responsibilities :

• Lead the design and delivery of AI-first solutions — setting the architectural standard for how LLMs, agents, and AI skills are composed into production‑grade systems.
• Architect and oversee AI harnesses that orchestrate multi‑step reasoning, tool use, memory, and retrieval across complex workflows.
• Design and build MCP (Model Context Protocol) connectors and integrations that extend AI agent capabilities into enterprise data sources and third‑party services.
• Evaluate and select the right foundation models, fine‑tuning strategies, and inference configurations for each use case.
• Define patterns for prompt engineering, context management, guardrails, and evaluation — and make them reusable across teams.
• Partner with engineering teams to translate architectural blueprints into implementable, maintainable code and infrastructure.
• Engage with executive and business stakeholders to communicate complex AI design decisions in plain language.
• Support pre‑sales for AI engagements — joining client conversations, shaping proposals, and articulating the technical credibility behind Systems' AI offerings.
• Lead and champion internal AI initiatives such as AI Ignite, driving experimentation, knowledge sharing, and a culture of continuous AI learning across the organisation.
• Work hand‑in‑hand with Confiz leadership to define and execute the strategy for becoming an AI‑native company — influencing how AI is embedded across delivery, operations, and go‑to‑market.
• Stay current with the fast‑moving AI landscape (models, frameworks, protocols) and proactively bring new capabilities to the table.

Requirements

• 10+ years of overall software or solutions architecture experience, with at least 3 years hands‑on with AI/ML systems in production.
• Direct experience building or working with AI harnesses and agent orchestration frameworks (e.g. LangChain, LlamaIndex, AutoGen, custom implementations).
• Hands‑on experience designing or consuming MCP connectors or equivalent plug‑in/tool‑use integration patterns.
• Deep familiarity with LLM APIs (OpenAI, Anthropic, Gemini, or open‑weight models) and the trade‑offs between them.
• Strong grasp of RAG pipelines, vector stores, semantic search, and knowledge retrieval architectures.
• Experience with modern cloud infrastructure (AWS, Azure, or GCP) and the ability to design scalable, cost‑efficient AI deployments.
• Excellent written and verbal communication skills — you can write a crisp architecture doc and present it to a non‑technical executive in the same morning.
• A bachelor’s degree in Computer Science or a related field is highly preferred.
• AI‑first by default: your first instinct for any new problem is to ask how an LLM or agent can solve it — and then apply sound judgment about when it shouldn't.
• Comfortable with ambiguity: you can drive to a decision when requirements are still forming.
• Pragmatic over dogmatic: you prefer shipped solutions to perfect designs that live in documents.
• Collaborative by nature: you raise the technical bar of everyone around you, not just your own output.

Nice to have:

• Experience with multi‑modal AI systems (vision, audio, structured data).
• Contributions to open‑source AI tooling or published writing on AI architecture.
• Familiarity with AI safety, responsible AI, and evaluation best practices.
• Experience in a product company or innovation‑led consultancy building AI products end‑to‑end.