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

• 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.

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.

• 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.

Fully remote, with a flexible schedule and the requirement to attend all key team and client meetings.

• 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.