Senior Solution Architect

The Role We are looking for a Senior Solution Architect who is a genuine technical generalist: someone who reasons from first principles, is insatiably curious about how things work under the hood, and can move fluidly between low-level implementation detail and high-level business narrative. You will own the technical shape of diverse client engagements - from cloud and full-stack platforms to modern AI and inference systems - and remain hands-on through delivery rather than handing designs over the wall.

This is a role for a builder-architect: equally comfortable whiteboarding a system with a principal engineer, debugging an inference bottleneck, or explaining a trade-off to a customer and internal teams in plain language.

What You'll Do

• Own end-to-end solution architecture for client engagements across cloud, data, full-stack, and AI/ML workloads, balancing technical rigour with cost, security, and time-to-value.
• Design from first principles — decompose unfamiliar problems, evaluate options on their merits, and justify decisions rather than defaulting to patterns or hype.
• Stay hands-on through delivery: prototype, review code and infrastructure, unblock engineering teams, and ensure what was designed is what actually ships.
• Simplify complex technical concepts for business, leadership, and customer audiences — translate architecture into outcomes, risks, and options stakeholders can act on.
• Lead client conversations, discovery workshops, and solution presentations; act as a trusted technical advisor and the bridge between engineering and the business.
• Architect AI-centric solutions — reason about model selection, inference architecture, tuning/optimization, and the systems that serve models in production.
• Coach and uplift teams — mentor engineers and associate architects, run design reviews, and raise the technical bar through teaching, not gatekeeping.
• Explore and prototype emerging technologies, bringing a spirit of creative, spontaneous problem-solving to engagements where the right answer isn't yet obvious.

Core Technical Competencies

• System design fundamentals — scalability, reliability, data modelling, consistency, latency, and the trade-offs between them.
• Cloud architecture and services — ideally AWS — including compute, storage, networking, serverless, and well-architected design principles.
• Full-stack development — backend and frontend, APIs, and the ability to read, write, and review production code with credibility.
• Software engineering best practices — version control, testing, design patterns, and maintainable, observable systems.
• DevOps and platform engineering — CI/CD, IaC, containerization, orchestration, and operational concerns across the delivery lifecycle.

AI & Inference Systems

• A solid, current understanding of how modern AI systems are built and operated — not just how to call an API:
• Underlying architecture of AI systems and inference pipelines — how models are served, scaled, and integrated into broader applications.
• Model lifecycle fundamentals — how model development, fine-tuning, and optimization work, and when each is appropriate.
• Practical grasp of inference economics and performance — latency, throughput, cost, quantization, and the levers that move them.
• Familiarity with GenAI patterns — retrieval-augmented generation, agentic workflows, evaluation, and grounding/guardrails.

Who You Are

• A first-principles thinker who questions assumptions and reasons up from fundamentals rather than relying on received wisdom.
• A curious generalist and technology enthusiast with a genuine drive to understand how things work under the hood.
• A creative, spontaneous problem-solver — comfortable improvising, prototyping, and finding non-obvious paths when the textbook answer falls short.
• A clear communicator who is well-spoken and equally effective with deeply technical engineers and non-technical executives.
• A natural teacher — someone who enjoys explaining, simplifying, and helping others level up.
• Strong attention to detail paired with the judgment to know when to zoom out to the bigger picture.

Experience & Qualifications

• 6–8 years of professional experience, with a strong foundation as a software developer/engineer.
• Demonstrated progression into solution architecture — associate, and ideally senior-associate level, exposure.
• A track record of delivering technical solutions in client-facing or product environments.
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
• Relevant cloud certifications (e.g., AWS Solutions Architect) are a strong plus.

Nice to Have

• Prior consulting or professional-services delivery experience.
• Hands-on experience with Bedrock, SageMaker, or comparable ML/GenAI platforms.
• Experience presenting at workshops, enabling teams, or producing technical thought-leadership content.