Solutions Architect (GenAI, Python/Data, AWS)

About the role

We are looking for a Solutions Architect who will design and deliver cloud‑native, data‑driven and LLM‑based AI solutions that address real client business challenges. The role combines deep technical expertise with client‑facing responsibilities, from presales to production delivery.

Key responsibilities

• Design and build cloud‑native data, generative AI and agentic solutions on AWS.
• Implement and optimise Retrieval‑Augmented Generation (RAG) systems for production use cases.
• Develop and maintain ETL/ELT workflows using modern orchestration and distributed computing tools.
• Write clean, production‑grade Python for AI integrations, backend services and RESTful APIs.
• Deploy ML and LLM solutions and implement MLOps/LLMOps/AgentOps practices (CI/CD, automated testing, model monitoring, experiment tracking).
• Support presales activities: discovery calls, technical proposals, scoping and client‑facing demos.
• Own the technical direction of engagements from discovery through delivery, acting as the trusted advisor for key stakeholders.
• Lead architecture reviews, produce design documents and contribute to technical standards.
• Mentor engineers, lead code reviews and share knowledge across the team.

Required profile

• Full‑stack mindset comfortable across AI, backend development and cloud infrastructure.
• Daily use of AI tools (e.g., Claude, Copilot) and proactive, self‑directed work style.
• Strong English communication (B2+), able to collaborate with distributed, multicultural teams.
• Experience leading client technical relationships, scoping ambiguous requirements and delivering phased plans.
• Proven ability to estimate costs and optimise cloud architecture spend.
• 7+ years building and operating production systems, not just demos or POCs.

Required skills

• Python (OOP, design patterns, clean architecture, performance optimisation)
• FastAPI, Django REST Framework or Flask for RESTful APIs
• Docker and Kubernetes
• AWS services: Bedrock, Lambda, ECS, S3, SQS, ECR (GCP experience considered)
• CI/CD pipelines (GitHub Actions, GitLab CI)
• LLM APIs: OpenAI, Anthropic, AWS Bedrock
• RAG system design and optimisation
• ETL/ELT workflow orchestration and distributed computing tools
• MLOps / LLMOps / AgentOps practices, model monitoring, observability and drift detection
• Cost estimation and cloud‑cost optimisation