Senior Solution Architect

Role Description

We are seeking a Solutions Architect to own and drive the technical architecture of our Environmental Accounting product area. This is a hands-on architecture role, ideal for a technically strong engineer who is ready to take ownership of system design, technical direction, and engineering standards while still contributing to the codebase.

You will play a key role in ensuring the engineering teams consistently deliver value to customers through high-quality, reliable, and scalable SaaS solutions. You will also act as the accountable technical authority for the product area within the wider organization, including Product Management, Engineering leadership, and senior stakeholders.

This role requires a strong software engineering background (ideally .NET / C# or Node.js) with the ability to stay close to the code while shaping architecture and influencing across teams. You should be comfortable working in an environment with minimal unnecessary formality, where success is driven through direct communication, trust, and continuous improvement.

Critically, this role demands deep, demonstrable proficiency with AI.

We are transforming how we build software and what we build. You will be expected to work fluently within an AI-Driven Development Lifecycle (AIDLC), to architect and deliver agentic AI solutions within our products, and to bring genuine hands-on experience with agentic coding tools — Claude Code in particular.

Key Responsibilities

• Own and evolve the technical architecture
• Define, document, and evolve the target architecture for Sphera's Corporate Sustainability product, covering design, scalability, performance, security, and maintainability
• Ensure architectural decisions are recorded, justified, and clearly communicated using appropriate modelling and documentation approaches
• Build a culture of technical excellence, sound design thinking, and continuous improvement across the engineering teams
• Act as the key technical point of reference for the product area, ensuring alignment with broader engineering and business goals
• Champion and embed an AI-Driven Development Lifecycle across the product area, applying AI meaningfully at every stage — requirements analysis, design, implementation, testing, code review, documentation, and operational support
• Define what "good" looks like for AI-assisted engineering: prompting and context practices, human review and verification gates, quality bars, and guardrails for what must never be delegated to a model
• Measure the impact of AI on delivery throughput, defect rates, and engineering experience, and iterate on practices based on evidence rather than hype
• Establish policy and standards for the safe, secure, and compliant use of AI tooling, including handling of proprietary code, customer data, and intellectual property
• Architect and deliver agentic AI capabilities within Sphera's Environmental Accounting product, taking them from prototype through to production
• Design robust agentic systems: tool and function calling, orchestration and multi-agent patterns, retrieval-augmented generation, memory and context management, and human-in-the-loop controls
• Own the non-functional dimensions of AI features — evaluation and testing strategy, observability, latency, cost management, model selection and versioning, and graceful degradation
• Address AI-specific risk directly: hallucination and grounding, prompt injection and data exfiltration, access control on agent tooling, auditability, and responsible AI considerations
• Maintain a working knowledge of the fast-moving AI landscape and make pragmatic build-versus-buy and model-selection recommendations
• Contribute directly to the codebase where it has the greatest impact, using modern software engineering practices
• Work hands-on with Claude Code and comparable agentic coding tools as a daily part of the role, and set the standard for how the wider team uses them effectively
• Extend and tailor the team's AI tooling — for example project context files, custom commands, subagents, hooks, and MCP server integrations with internal systems
• Provide deep technical guidance in .NET / C# or Node.js environments
• Prototype and validate architectural approaches, spiking on high-risk or high-uncertainty areas ahead of delivery
• Maintain a strong, current understanding of system design, distributed systems, cloud platforms, and software quality
• Partner closely with Product Management to translate requirements into clear, actionable technical designs and engineering outcomes
• Ensure architectural work supports consistent, high-quality SaaS releases aligned with business priorities
• Identify and remove technical blockers, proactively resolving design and integration issues impacting team performance
• Balance long-term architectural health against near-term delivery needs, making trade-offs explicit
• Raise the technical capability of engineers through design reviews, pairing, and mentoring
• Upskill the team in AI-assisted engineering, coaching engineers to use agentic tooling with judgment — and to critically review what it produces
• Set and champion engineering standards for code quality, testing, observability, and security
• Have open, honest, and sometimes challenging technical conversations to drive better outcomes
• Foster a culture of informal, direct communication and trust across the teams
• Take ownership of technical practices and evaluate their effectiveness in delivering outcomes
• Apply critical judgment to existing designs and processes and propose improvements where needed
• Identify and drive down technical debt, prioritizing work that materially improves reliability and speed of delivery
• Ensure the teams operate efficiently without unnecessary bureaucracy, while maintaining appropriate engineering discipline
• Act as the primary technical voice for the product area to internal stakeholders and customers where required
• Maintain strong working relationships with Product, Engineering, and wider business functions
• Ensure transparency in technical direction, risks, and trade-offs

Qualifications

• 7+ years of software engineering experience
• 3+ years in an architecture, lead engineer, or principal engineer capacity
• Strong hands-on development experience in .NET / C# or Node.js
• Proven experience architecting and delivering SaaS or enterprise software products at scale
• Strong experience with cloud platforms (Azure or AWS), distributed systems, and API design
• Strong ability to translate product requirements into technical designs and execution
• Demonstrated experience in mentoring engineers and influencing technical direction without direct authority

Requirements

• Technical proficiency with Databricks, SQL Server, and Azure SQL (highly desirable)
• Demonstrable hands-on experience with Claude Code or equivalent agentic coding tools, used routinely on real production codebases rather than experimentally
• Practical experience operating within an AI-Driven Development Lifecycle (AIDLC), applying AI across design, implementation, testing, review, and documentation
• Solid working understanding of agentic AI design patterns — tool/function calling, orchestration, retrieval-augmented generation, and human-in-the-loop design
• Working knowledge of the current LLM landscape and the judgment to select models and approaches appropriate to the problem, cost, and latency profile
• Experience defining evaluation, testing, and observability strategies for non-deterministic AI systems
• Understanding of AI-specific security and governance concerns, including prompt injection, data leakage, access control on agent tooling, and responsible AI practice
• Genuine enthusiasm for AI as a force multiplier, balanced with the engineering rigor to know when not to use it

Benefits

• Remote work flexibility
• Inclusive work environment
• Equal Opportunity Employer