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.
- 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.
- 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.
- 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, onboarding of teams (incl. SPD Labs), upskilling engineers and AI champions, and monitoring adoption while answering questions along the way.
- Drive system innovation by leveraging AI as a core enabler — prototyping high-impact capabilities to prove technical feasibility and steer future-ready architectural directions.
- Provide early, defensible ballpark estimates using AI tooling to help qualify opportunities before deeper discovery.
- We’re looking for you if you have
- 7+ years of hands-on software development experience with strong core engineering principles, including 3+ years designing complex software architectures for multi-stack environments.
- Broad, hands-on engineering experience across the full SDLC: distributed systems, cloud-native architectures, scalable APIs, data-driven solutions and enterprise integrations.
- Practical experience designing data solutions across relational databases, distributed caching, replication and enterprise search engines.
- Experience architecting on at least one major cloud platform using containerization and orchestration, plus a working map of managed cloud services and the habit of continuously learning new platforms, frameworks and libraries — commercial and open source.
- Practical, hands-on understanding of AI/GenAI and agentic development — agents, LLM behaviour, context management, AI coding harnesses and their trade-offs. This is central to the role.
- Ability to work effectively alongside ML engineers on AI/ML solutions (OCR/Computer Vision, fraud detection, RAG, agentic) — enough to scope, challenge and estimate, without being a deep ML modelling specialist.
- A disciplined approach to architectural decision-making: systematically evaluating trade-offs, risks and technology pros/cons to deliver resilient, cost-effective solutions.
- Strong estimation skills and the ability to defend estimates to both Sales and Delivery.
- Proven ability to manage stakeholder expectations and build cross-organizational alignment, adapting from deep technical dives with engineers to non-technical dialogue with senior stakeholders.
- Excellent communication and client-facing skills; people-oriented and comfortable guiding, onboarding and upskilling engineers — enablement is a core part of the job.
- Strong analytical and problem-solving skills, with the ability to define technical solutions under tight deadlines and vague requirements.
- Prior experience in pre-sales, technical discovery or solution consulting.
- Experience coaching engineering teams and growing their ability to make high-quality autonomous software-design decisions.
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Pre-screening with recruiter
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Technical Screening.
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Technical Interview
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Manager Interview
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Interview with BizDev & Sales.
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Executive Interview.
Oksana Shulha Senior Talent Acquisition Specialist
