Solutions Engineer supporting global solutions engineering team with demos and technical presentations. Collaborating with partners to customize solutions and facilitate product development based on feedback.
About the role
- Consultant.e Solutions Architect specializing in Cloud and GenAI architecture for Thiga's R&D task force. Overseeing the design of scalable and secure cloud-native platforms.
Responsibilities
- Architect Cloud & GenAI platforms: design reference architectures (multi-tenant, event-driven, API-first) to deliver products in a robust and secure way
- Cost / Latency / Reliability optimization: define performance and FinOps strategies (caching, autoscaling, serverless vs containers, CPU/GPU scheduling, multi-model approaches for AI) and measure their impact on SLOs
- Data & retrieval on cloud: design the data foundation for cloud and GenAI workloads
- Build vs buy approaches: evaluate and integrate managed vs self-hosted solutions, using a pragmatic decision framework
- Industrialization (DevOps / SRE / Platform Engineering): establish a delivery platform (CI/CD, GitOps, IaC) and reproducible environments for traditional cloud applications and AI workloads
- Security & compliance by design: implement IAM, secrets management, encryption, network policies, auditing, data boundaries, and optionally for AI red teaming and GenAI guardrails (content filtering, prompt-injection mitigation, output constraints)
- Architecture audits: analyze client platform scalability (quotas, rate limiting, contention, costs, availability, DR/BCP) and propose modernization roadmaps
Requirements
- Architect / engineer (junior to senior): you have built and possibly operated distributed systems in production (scalability, reliability, security), not just prototypes
- Cloud & DevOps expert: solid experience with at least one hyperscaler (AWS/Azure/GCP), containers, Kubernetes, networking, IAM, CI/CD, IaC (Terraform/Pulumi), GitOps. Certifications are a plus
- Excellent communicator: you can frame problems, simplify, persuade and support multiple teams (product, security, data, dev) without dogmatism
- SRE-minded & pragmatic: you think in terms of SLOs, error budgets, observability and resilience (chaos engineering, DR), and can balance time-to-market vs robustness
- GenAI-aware: you understand the fundamentals of GenAI industrialization (RAG, evaluation, prompt/tooling, governance, security) and can integrate these components into a cloud platform
Benefits
- An opportunity to build production-grade Cloud & GenAI platforms, not just demos
- A technical environment that values operational excellence and clean architecture
- A team that loves hard tech, pragmatism, and end-to-end ownership
- An R&D budget to test the latest Cloud/GenAI technologies (gateways, vector DBs, LLMs, observability, GPUs) and run benchmarks
- The chance to define the architectural patterns (Cloud/DevOps/GenAI) for the coming years
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