Solutions Architect (GenAI, Python/Data, AWS)

About the role

We are looking for a Solutions Architect who will design and deliver cloud‑native, generative‑AI solutions that solve real business problems. The role combines AI research, backend development, and cloud infrastructure to create production‑grade LLM‑based and agentic applications.

Key responsibilities

• Design and implement cloud‑native data pipelines, Retrieval‑Augmented Generation (RAG) systems, and agentic AI solutions on AWS.
• Write clean, production‑ready Python code for AI integrations, backend services, and RESTful APIs (FastAPI, Django REST, Flask).
• Build and maintain ETL/ELT workflows using modern orchestration tools and distributed computing.
• Deploy and operate ML/LLM models with MLOps, LLMOps, and AgentOps practices (CI/CD, automated testing, model monitoring, experiment tracking).
• Lead architecture reviews, produce technical design documents, and define standards for AI projects.
• Act as a trusted technical advisor for key customer stakeholders, supporting presales activities, discovery calls, technical proposals, and client‑facing demos.
• Mentor engineers, conduct code reviews, and share knowledge across the team.

Required profile

• 7+ years of experience building and operating production systems, including LLM‑based applications.
• Full‑stack mindset comfortable across AI, backend development, and cloud infrastructure.
• Proactive, self‑directed attitude with strong problem‑solving skills.
• Excellent English communication (B2+) and experience working with distributed, multicultural teams.
• Proven ability to own client technical relationships, lead discovery, and create scoped delivery plans.

Required skills

• Python (OOP, design patterns, clean architecture, performance optimization)
• FastAPI, Django REST, Flask
• Docker, Kubernetes
• AWS services (Bedrock, Lambda, ECS, S3, SQS, ECR)
• GCP (considered)
• CI/CD pipelines (GitHub Actions, GitLab CI)
• LLM APIs (OpenAI, Anthropic, AWS Bedrock)
• Retrieval‑Augmented Generation (RAG) systems
• MLOps / LLMOps / AgentOps practices
• Model monitoring, observability, drift detection
• Cost estimation and cloud‑architecture cost optimization