Senior Python Backend & AI/LLM Engineer

Senior / Staff Python Backend & AI/LLM Engineer

Architecture, Applied AI, and Agentic Development

***Hands-on senior+ IC role 70% engineering focus Python, AI systems, architecture, and ADLC***

Company Description

SANNOS is an intelligence layer for global compliance, providing an automated assessment and risk generation engine trusted by Big 4 firms and major institutions to manage complex regulatory environments. Founded by risk and compliance experts Ralph Bengtsson and Anders Søborg, the company was created from real-world experience on the frontlines of global regulation. SANNOS bridges high-stakes executive decision-making with advanced computing to automate audit and compliance reviews in minutes instead of days. The platform helps organizations turn compliance from a bureaucratic burden into a strategic advantage, supporting frameworks such as ISO27001, DORA, NIS2, NIST, CMMC, Secure Control Framework (SCF), and CSRD. Team members join a mission-driven environment focused on clarity, confidence, and innovation in compliance technology.

The Role

We’re looking for a Senior Python Backend & AI/LLM Engineer who can build production systems, make sound architecture decisions, and create an agentic developer platform or a smaller version of one.

This is not a ticket-only backend role. You’ll own problems from design through production and help build reusable agent workflows, runtime integrations, safe updates, evaluation, and observability. You should use coding agents and LLMs throughout the Agentic Development Lifecycle (ADLC) while taking full responsibility for quality, security, and technical decisions.

You should be able to show what you personally built, why you designed it that way, how it performed in production, and what business or engineering result improved.

This is posted as a band. Whether you join at Senior or Staff level is decided at offer, based on the interview loop and the scope you have owned before — not by the title on this post.

What You Will Do

• Spend about 70% of your time on hands-on engineering, including Python code, system design, AI workflows, debugging, and technical reviews.
• Design and operate Python APIs and services for users, devices, access rights, workflow catalogs, versioned releases, and telemetry.
• Package reusable AI workflows using skills, specialized agents, hooks, rules, tools, and supporting files.
• Build adapter layers that deliver the same workflows to different coding-agent runtimes and handle capability gaps safely.
• Orchestrate sequential and parallel agent tasks with shared state, isolation, retries, checkpoints, resumable work, and human approval.
• Build safe lifecycle operations with previews, file ownership tracking, conflict detection, snapshots, updates, and rollback.
• Create project context, agent instructions, tool contracts, and evaluation checks that produce consistent engineering results.
• Track workflow success, latency, failures, token usage, cost, and developer time saved; use the data to improve the system.
• Protect secrets and user data through safe defaults, permission boundaries, redaction, audit logs, and review gates.
• Use AI daily across ADLC and raise the team’s level through architecture reviews, code reviews, mentoring, and practical standards.
• Engage on architectual decisions

Your Experience - Mandatory:

• 8+ years in software engineering, with direct ownership of production backend systems.
• 7+ years building Python services at production scale, using FastAPI, Django, Flask, or similar frameworks.
• 3+ years working with LLMs or coding agents in real projects, with at least 2 of those running in production - agent instructions, tool use, context design, and evaluations. Applied LLM engineering is a young field, so we weigh what you shipped more heavily than how long you have been doing it.
• Hands-on experience orchestrating multi-step or multi-agent workflows with shared state, parallel work, retries, failure recovery, and human approval.
• At least one tool, service, or internal platform you personally designed, built, and released — and can walk us through end to end.
• Strong fundamentals in API design, async work, idempotency, databases, object storage, queues, testing, cloud infrastructure, CI/CD, and observability.
• Practical security judgment for AI systems: secrets handling, prompt injection, data exposure, permission boundaries, model limits, token cost, and unsafe too…