Solution Architect (Pre-Sales, Delivery & AI Enablement)

About this role

At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed. And now we are looking for a Solution Architect (Pre-Sales, Delivery & AI Enablement) to join us as part of our team. About the role This is an internal architecture role at the intersection of pre-sales, delivery and AI enablement. Around 60% of our incoming pre-sales requests now involve AI/ML (OCR, Computer Vision, fraud detection, RAG and agentic solutions), so we need an architect who can shape, challenge, estimate and sell these solutions — not a narrow ML specialist. You will work across multiple engagements and domains rather than a single product, and collaborate closely with our in-house ML/MLOps team rather than owning deep ML modelling yourself. The role has three focuses: • Pre-Sales & Estimation — shaping and defending solutions and estimates for new engagements. • Solution Architecture & SDLC ownership — owning end-to-end architecture and the overall target-architecture vision across engagements. • AI Enablement — helping teams adopt AI-assisted development: selecting the right tools/harness per project, onboarding, upskilling, and monitoring adoption. Technical stack • Languages & platforms: Java, C#, Python, Node.js — multi-stack breadth, without being locked into one ecosystem. • Architecture & integration: microservices, event-driven and messaging patterns, scalable APIs, enterprise integrations, data-analytical systems. • Cloud: AWS, GCP or Azure; managed IaaS/PaaS/SaaS services. • Containers & orchestration: Docker, Kubernetes. • Data & search: PostgreSQL, MS SQL, distributed caching and replication (Redis), enterprise search (Elasticsearch), vector stores for retrieval. • AI/GenAI: LLM orchestration, multi-agent frameworks, RAG pipelines, evaluation and guardrails; OCR/Computer Vision and fraud-detection solutions delivered together with ML engineers. • AI-assisted development: AI coding tools and harnesses (e.g., BMAD, Spec Kit), agentic workflows, context management. • Architecture practices: ADRs, target/transition-state and system diagrams, NFRs and the "-ilities", secure-by-design, CI/CD. Work Environment Fully remote, with a flexible schedule and the requirement to attend all key team and client meetings. As a qualified expert, You will 1. Pre-Sales & Estimation • Lead the technical discovery process and design robust, scalable, cost-effective solutions for proposals, RFI/RFP responses and new client engagements. • Own estimation end-to-end: turn Sales’ discoveries into engineering-grounded estimates; challenge and correct estimates that are not grounded in real delivery effort. • Act as the bridge between Sales and Delivery — ensure estimates reflect engineers’ input and that the delivery team understands and can execute what was sold. • Prepare technical sections of proposals: solution descriptions, architecture diagrams, assumptions, risks and delivery approach. • Be the primary technical point of contact for prospective clients, clearly articulating the solution, technology stack and implementation strategy to technical and non-technical stakeholders. • Produce an architecture vision, roadmap, MVP definition and high-level delivery plan during discovery/inception. • Where AI/ML is involved, scope and estimate it correctly, working with our in-house ML engineers for deep modelling input. 2. Solution Architecture & Delivery • Own and evolve the end-to-end architecture of solutions (backend services, data storage, integrations, cloud infrastructure), explicitly addressing the "-ilities": scalability, availability, recoverability, maintainability, extensibility, portability, usability and security. • Hold and promote the target architecture vision across the portfolio; run architecture reviews and design workshops with delivery teams. • Drive system evolution toward well-defined target and transition (interim) states, using system diagrams that give engineering teams a clear, actionable execution path. • Propose pragmatic, balanced technical decisions in areas such as build vs. buy, now vs. later and refactor vs. rebuild, and document the trade-offs behind them. • Define and maintain architecture artefacts: high-level and system diagrams, data flows, non-functional requirements, technical guidelines and ADRs — and author AI-ready solution specifications as part of the design lifecycle. • Provide hands-on guidance: design sessions, review of critical technical decisions and PRs, spikes and PoCs when needed. • Ensure the solution meets scalability, security, availability and cost-efficiency expectations, and simplify otherwise complex problems into pragmatic designs. • Participate in starting new projects from scratch: scope, architecture approach, MVP slice and key technical decisions. 3. AI Enablement (a key differentiator for this role) • Understand AI-assisted development deeply: how agents and agentic workflows work, how LLMs behave, context management, and the trade-offs/gaps of AI coding tools and harnesses (e.g., BMAD, Spec Kit) — including limitations such as incomplete TDD, combined dev+test single-agent roles, and context handling. • Recommend which AI dev tools/harness fit which class of project, phase and SDLC — and where they do not; identify gaps and judge whether they are critical for a given project. • Customize and guide the harness and agent setup to fit a team’s SDLC, rather than blindly adopting a ready-made flow. • Support AI enablement across projects: initial guidance

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