We are looking for an experienced Solution / Technical Architect to provide technical leadership across strategic cloud, data, analytics, and AI-enabled initiatives. The role will own end-to-end solution architecture covering applications, data, integration, cloud platforms, security, observability, and operations, while supporting PoCs, MVPs, tenders, and customer-facing opportunities.
Key Responsibilities
- Translate business and customer requirements into secure, scalable, and practical solution architectures.
- Own end-to-end architecture across application, data, integration, cloud, security, observability, and operations.
- Lead solution shaping for new programmes, PoCs, MVPs, tenders, and customer opportunities.
- Assess feasibility, technical risks, dependencies, assumptions, estimates, and technology trade-offs.
- Design batch and streaming data pipelines, APIs, event-driven integrations, data warehouses/lakehouses, analytics, and reporting solutions.
- Design and guide AI/GenAI solutions, including RAG, agentic AI, model integration, evaluation, guardrails, observability, and LLMOps.
- Lead hands-on PoCs/MVPs to validate architecture and technology decisions before production implementation.
- Recommend platforms and technologies based on cost, performance, security, scalability, maintainability, and operational requirements.
- Embed security and privacy by design, including IAM, OAuth2/OIDC, secrets management, data protection, classification, compliance, and auditability.
- Guide engineering teams through technical designs, implementation reviews, code/design reviews, backlog elaboration, and technical governance.
- Develop architecture documentation, solution diagrams, reusable patterns, reference implementations, and technical decision records.
- Work closely with technical and non-technical stakeholders and communicate architecture options, risks, dependencies, and recommendations clearly.
- Manage multiple opportunities and maintain strong client relationships and expectations.
Must-Have Qualifications
- Bachelor’s degree or equivalent.
- 12+ years of enterprise application or data platform architecture experience.
- At least 2 years of production-grade GenAI architecture experience.
- Strong exposure to Data Science and Machine Learning.
- Minimum 3 years of hands-on experience in each of AWS, GCP, and Azure.
- At least 1 year of hands-on experience with GenAI / Agentic AI technologies.
- Strong experience with containers/Kubernetes or serverless and IaC tools such as Terraform or CloudFormation.
- Hands-on experience with at least one Agent/RAG framework, such as:
- LangChain / LangGraph
- DSPy
- OpenAI / Anthropic tool use
- Databricks Agents
- Equivalent technologies
- Practical LLMOps experience covering evaluation, tracing/observability, prompt/version management, and CI/CD for AI solutions.
- Strong lakehouse and data engineering knowledge, including Delta/Iceberg/Hudi, streaming technologies such as Kafka/Kinesis/Pub/Sub, and vector databases/search.
- Strong API and integration architecture experience across REST, gRPC, GraphQL, APIs, and event-driven architectures.
- Strong understanding of OAuth2/OIDC, JWT, mTLS, secrets management, data governance, lineage, access policies, and AI guardrails.
- Good understanding of mobile and web application architectures.
- Strong communication, stakeholder management, problem-solving, and client-facing skills.
- Ability to manage multiple opportunities and work effectively across organisational boundaries.
Required Certifications
- AWS / Azure / Google AI Certification
- AWS / Azure / Google Data Science Certification
- AWS / Azure / Google Solution Architect Certification
- TOGAF 9 – The Open Group
- Any other equivalent industry-standard architecture certification