Backend Engineer – Python

wattgen is an AI consulting and engineering firm that helps organisations understand where AI can make a real difference – then build the systems that deliver it. We pair thoughtful problem-solving with engineering that works in the real world.

Role Overview

In this role, you'll build intelligent systems that large businesses can rely on. You'll develop reliable, production-ready AI applications and tackle hard problems by reasoning from first principles. Day-to-day, you'll collaborate with colleagues across Product Design, Data Engineering, Data Science, and Infrastructure.

Responsibilities

• Design, develop, and ship software that applies AI to real-world problems, with a focus on automating complex manual workflows in large businesses.
• Build backend services and data pipelines, owning the full lifecycle – architecture, implementation, deployment, and ongoing maintenance.
• Apply an AI-augmented SDLC and AI-assisted tooling to accelerate delivery, with human oversight built in at the points where correctness and judgment matter most.
• Design modular, scalable, reusable APIs (REST, gRPC, event-driven) and backend architectures that hold up to the uptime demands of large-scale businesses.
• Break down complex, loosely defined problems into clear technical designs, and deliver production-grade systems in short sprint cycles without compromising quality.
• Apply profiling, observability, robust architectural design, and ongoing iteration to improve latency, throughput, and cost efficiency.
• Team up with Product Design, Data Engineering, Data Science, Infrastructure, and client-facing teams to deliver systems that are intuitive, reliable, and easy for both our own and our clients' developers to adopt.
• Review code, share knowledge, and lead by example through high-quality engineering practices.
• Monitor and maintain deployed applications so they keep delivering value over time.
• Stay current with the latest developments in software engineering and AI.

Requirements

• 8+ years delivering production systems, with deep expertise in distributed systems (scalability, fault tolerance, and performance).
• Expert-level Python, with a track record of building and maintaining large-scale production codebases.
• Deep experience with real-time or streaming data systems.
• Solid understanding of relational databases, data pipelines and how data flows into production AI applications.
• Strong command of Docker, Kubernetes, and cloud-native service deployment.
• Sharp problem-solving, with the ability to reason from first principles and navigate ambiguity.
• Excellent communication and stakeholder management, with experience influencing across teams.
• Comfortable working in fast-paced environments and balancing competing priorities.

Nice to Have

• Experience with modern LLM frameworks and tooling for building, orchestrating, and evaluating AI systems.
• Experience with vector databases.
• Background in AI/ML platforms or data-intensive systems at scale.

Driving Operating Model Transformation with Applied AI