I’m building a green-field, AI-powered SaaS platform and need a senior back-end engineer who can also wear the architect hat. The core of the system will be written in Python and must support advanced Natural Language Processing and Machine Learning flows from day one. Scope You’ll own the full server-side design, coding and deployment pipeline: crafting microservice boundaries, defining data models, setting up CI/CD, and ensuring every component scales securely in AWS. The stack will lean on AWS Lambda for serverless compute, Amazon S3 for object storage, and Amazon RDS for transactional data, so deep familiarity with those services is essential. AI Layer We’ll plug in an agent-style architecture that orchestrates multiple models, so experience wrapping and serving models through Python (FastAPI, Flask, or similar) and optimising inference workloads is key. Expect to integrate vector stores, background task queues, and a feature store as the product matures. Security & Compliance Everything must be designed with SOC 2 in mind—centralised logging, IAM least-privilege, encryption at rest and in transit, and automated policy checks. DevOps Terraform (or CDK), Docker, and GitHub Actions are already in the toolbox. You’ll refine and extend this setup, baking in blue/green deploys and zero-downtime migrations. Deliverables • High-level architecture diagram and tech stack rationale • Production-ready Python codebase with NLP & ML modules wired in • IaC scripts provisioning Lambda, S3, RDS and supporting resources • Security documentation mapping controls to SOC 2 requirements • Runbook and CI/CD pipeline definitions If you thrive on shaping scalable, compliant AI backends from scratch, let’s talk about timelines and milestones.