Solution Architect – AI and Automated Solutions

Sophos is a cybersecurity leader defending 600,000 organizations globally with an AI-driven platform and expert-led services. Sophos meets organizations wherever they are in their security maturity and grows with them to defeat cyberattacks. Its solutions combine machine learning, automation, and real-time threat intelligence with frontline human expertise from Sophos X-Ops to deliver advanced, 24/7 threat monitoring, detection, and response.

 

Sophos offers industry-leading managed detection and response (MDR) alongside a comprehensive portfolio of cybersecurity technologies — including endpoint, network, email, and cloud security, extended detection and response (XDR), identity threat detection and response (ITDR), and next-gen SIEM. Together with expert advisory services, these capabilities help organizations proactively reduce risk and respond faster, with the visibility and scalability needed to stay ahead of evolving threats.

 

Sophos goes to market with a global partner ecosystem, including Managed Service Providers (MSPs), Managed Security Service Providers (MSSPs), resellers and distributors, marketplace integrations, and cyber risk partners, giving organizations the flexibility to choose trusted relationships when securing their business. Sophos is headquartered in Oxford, U.K. More information is available at www.sophos.com.

The Solution Architect – AI and Automated Solutions partners with enterprise customers to design and deliver intelligent solutions that automate workflows, improve knowledge access, and support new product and operational capabilities. This role owns the technical journey from discovery and architecture through prototyping, production deployment, and continuous improvement.

The position works closely with business and engineering stakeholders to translate ambiguous requirements into scalable solutions integrated with existing applications, data platforms, and collaboration tools. The role may leverage cloud AI platforms such as Amazon Bedrock or Amazon SageMaker and, where appropriate, productivity ecosystems such as Microsoft 365 Copilot.

Solution Architecture and Stakeholder Collaboration

Lead discovery sessions to identify high-value use cases for AI and automated solutions, including workflow automation, copilots, knowledge retrieval, and intelligent routing.

Translate business objectives into technical designs covering workflows, data flows, integration points, and non-functional requirements such as latency, reliability, security, and compliance.

AI and Automated Solution Design and Implementation

Design and implement AI Agentic solutions that can reason, plan, call tools, and execute multi-step tasks across APIs, databases, SaaS platforms, and document repositories.

Build solution workflows using patterns such as planner-executor, supervisor-worker, and human-in-the-loop with frameworks including LangChain, LangGraph, and similar orchestration libraries such as LlamaIndex and Semantic Kernel.

Use MCP-style protocols to standardize tool discovery and invocation, including tool schemas, validation, retries, error handling, and safe execution patterns.

Implement retrieval-augmented generation pipelines, including ingestion, chunking, metadata design, embeddings, vector indexing, hybrid search, reranking, and retrieval orchestration.

Apply prompt and policy design techniques, including structured prompting, guarded templates, reasoning aids, and evaluation approaches to improve quality and reduce hallucinations.

Experience integrating with cloud AI services and model platforms such as Amazon Bedrock, Amazon SageMaker, third-party LLM providers, or self-hosted open-source models.

Experience with Microsoft Copilot Studio for building and extending enterprise AI solutions, including custom workflows, connectors, REST/API integrations, grounding with enterprise knowledge, and MCP-enabled integrations.

Comfortable working in Python for AI application development, orchestration, and integration use cases.

Hands-on experience building LLM-powered applications, including prompt engineering, tool-calling, structured outputs, and multi-step conversational workflows.

Experience designing and implementing RAG systems, including document ingestion, chunking, metadata strategies, vector stores, hybrid search, and reranking.

Familiarity with orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent platforms.

Proven experience as a Solution Architect, Lead Engineer, or Senior Engineer designing end-to-end enterprise solutions.

Experience with at least one major cloud platform, ideally AWS, including networking, IAM, container orchestration, and serverless services.

Experience instrumenting AI solutions with logging, response capture, monitoring, and feedback loops for continuous improvement.