Solution Architect

Role Description

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.

Qualifications

• 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.

Requirements

• 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.
• 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.
• 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.

Benefits

• Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling client growth.
• Enjoy the freedom of fully remote work with a flexible working schedule.
• Empower yourself with a stable workload and a stable income, supported by provided laptops and licensed software.
• Benefit from performance and merit reviews, elevate your skills with personal development plans, and individual learnings through the corporate library, public speaking support, and more.
• Work with a team of one mind who cares about what they do and how they do.
• Collaborate with top-notch experts who are always ready to help and support you through any challenges.
• Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values.
• Feel supported by your HR, and take advantage of our referral bonus program.