Senior Presales Solutions Architect

About this role

Job Purpose
The Senior Presales Solutions Architect is responsible for leading the technical presales lifecycle for opportunities involving digital solutions, enterprise platforms, microservices, artificial intelligence, data, analytics, business intelligence and telecom solutions.
The role works closely with sales, delivery, product, architecture, finance, partners and customers to understand business needs, define suitable solution approaches, design robust technical architectures, prepare high-quality technical proposals, estimate implementation efforts and present proposed solutions to executive, business and technical stakeholders.
The position requires strong technical architecture capabilities, telecom industry knowledge, proposal development experience, commercial awareness, stakeholder management and customer presentation skills.

Key Responsibilities
1. Opportunity Qualification and Presales Planning
Review customer opportunities, RFPs, RFIs, tenders, statements of work and business requirements.
Assess opportunities from technical, delivery, resource, commercial and strategic perspectives.
Participate in Go/No-Go evaluations and provide clear technical recommendations.
Identify missing information, dependencies, risks, assumptions and clarification questions.
Develop presales response plans, technical workstreams, responsibilities and submission timelines.
Coordinate with sales, PMO, delivery, finance, legal, cybersecurity and external partners.
Evaluate whether each opportunity is aligned with the organization’s capabilities, technologies, resources and strategic direction.
Identify technical gaps and recommend suitable partners or subcontractors where required.
2. Requirements Analysis and Solution Definition
Conduct discovery sessions and workshops with customers and internal stakeholders.
Translate business requirements into functional and non-functional technical requirements.
Understand customer challenges, existing platforms, integration landscapes, data sources, operational processes and future objectives.
Define suitable use cases across digital solutions, microservices, AI, data, BI and telecom domains.
Prioritize use cases based on value, feasibility, cost, implementation complexity and expected outcomes.
Identify opportunities for automation, process improvement, intelligent services, analytics and customer experience enhancement.
Ensure requirements are clear, measurable, traceable and aligned with the proposed solution.
Prepare requirement-traceability and compliance matrices.
Validate requirements with business, technical, security and operational stakeholders.
3. Digital Solution Architecture
Design end-to-end digital solution architectures covering enterprise portals, web applications, mobile applications, content management, workflows, integration, data, security, infrastructure and operations.
Prepare high-level architecture, logical architecture, system-context diagrams, integration diagrams, deployment views and data-flow diagrams.
Recommend suitable technologies, frameworks, platforms and architectural patterns.
Ensure proposed solutions are scalable, secure, resilient, maintainable, observable and cost-effective.
Design solutions for cloud, on-premises, hybrid and sovereign deployment environments.
Define availability, performance, scalability, backup and disaster-recovery requirements.
Ensure technical designs comply with customer architecture standards, security policies and operational requirements.
Define reusable components and shared technical services.
4. Microservices and Integration Architecture
Design microservices-based architectures using domain-driven design, API-first principles and event-driven patterns.
Define service boundaries, APIs, integration contracts, data ownership and communication mechanisms.
Design synchronous and asynchronous integrations using REST APIs, API gateways, message brokers, event streaming and enterprise integration platforms.
Recommend suitable containerization and orchestration approaches using Docker, Kubernetes or equivalent technologies.
Define requirements for service discovery, centralized configuration, secrets management, monitoring, logging, tracing and fault tolerance.
Address scalability, availability, performance, security and disaster-recovery requirements.
Support the modernization of legacy applications and monolithic systems.
Recommend suitable CI/CD, DevSecOps and automated testing approaches.
Define API governance, versioning, security and lifecycle-management practices.
5. Artificial Intelligence Solutions
Identify and define practical AI and generative AI use cases based on customer requirements.
Design AI solution architectures covering machine learning, natural language processing, computer vision, forecasting, recommendation, classification and intelligent automation.
Define generative AI solutions using large language models, retrieval-augmented generation, embeddings, vector databases, knowledge bases, fine-tuning and AI agents.
Design AI integration approaches with customer systems, databases, APIs, documents and enterprise platforms.
Define data preparation, model training, evaluation, deployment, monitoring and continuous-improvement approaches.
Address AI governance, privacy, security, explainability, bias, hallucination control and human approval requirements.
Evaluate cloud-based, on-premises and hybrid AI deployment options.
Support AI demonstrations, pilots, prototypes and proof-of-concepts.
Estimate infrastructure, storage, compute and GPU requirements for AI workloads.
Define measurable AI success criteria, expected business value and operational outcomes.
6. Data and Business Intelligence Solutions
Design business intelligence, analytics, reporting and data-visualization solutions.
Define data integration and processing approaches using ETL, ELT, APIs, streaming and data pipelines.
Design conceptual architectures for data warehouses, data lakes, lakehouses, operational data…