Senior Back End Engineer

Design, implement and maintain the core backend architecture for Elixirr’s AI-augmented consulting platform and agent platform. 

Drive decisions on microservices, containerization and serverless solutions (e.g., AWS Lambda, Azure Functions, ECS, AKS) based on performance, cost and scalability requirements. 

Own services end-to-end: architecture, implementation, testing, deployment, observability and on-call. 

Operate fluently across a modern, layered AI developer stack: editor-level copilots, agentic coding tools, repo-aware assistants, AI in CI (test generation, security and performance checks) and AI-assisted product discovery. 

Practice spec-driven, harness-based development: write precise specifications, acceptance criteria and context briefs that let AI tools generate useful code instead of guessing. 

Treat prompts, agents and evals as first-class engineering artifacts — versioned, reviewed and tested like any other code. 

Hold the quality bar high on AI-generated code: review rigorously for correctness, security, edge cases, performance and long-term maintainability. Volume comes from AI; quality comes from engineers. 

Build and maintain the internal guardrails — eval harnesses, security checks, policy controls — that make AI-assisted development safe and repeatable for the rest of the team. 

Help the team measure impact with clear engineering metrics (deployment frequency, cycle time, change failure rate, defect density) so we know where AI is actually helping. 

Implement robust security measures — OAuth 2.0, OIDC, JWT, fine-grained authorization, secrets management, data protection — by default, not as an afterthought. 

Address the specific risks of AI-assisted development: prompt injection, insecure AI-generated code, hallucinated dependencies, and supply chain risks from AI developer tooling. 

Ensure compliance with relevant industry regulations and Elixirr’s internal data governance standards (e.g., SOC 2, ISO 27001, GDPR). 

Work closely with product, DevOps, front-end, AI engineers, data scientists and client-facing consultants to turn ambiguous problems into shipped product. 

Communicate architectural decisions and technical trade-offs clearly to both technical and non-technical stakeholders. 

Set up and maintain logging, tracing, metrics and alerting — including LLM-specific observability (prompts, tool calls, tokens, cost, latency) — so the platform is understandable and debuggable in production. 

Diagnose and resolve bottlenecks, and make the platform scale as client and internal usage grows. 

Mentor engineers across our UK, Croatia and South Africa teams — especially on how to work effectively alongside AI tooling. 

Advocate for best practices in cloud-native development, CI/CD and AI-enhanced workflows. 

Bachelor’s or Master’s in Computer Science, Engineering or a related field — or equivalent professional experience. 

5+ years building and operating large-scale backend systems in cloud environments (AWS and/or Azure). 

Strong proficiency in Python and/or at least one major backend language/framework (e.g., FastAPI, Django, Java/Spring Boot, Node.js). 

Experience with microservices, RESTful API design, event-driven and serverless architectures. 

Familiarity with containerization (Docker, Kubernetes) and Infrastructure as Code (Terraform, CloudFormation, ARM/Bicep). 

Solid understanding of relational and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, DynamoDB, Cosmos DB). 

Demonstrable daily use of AI-assisted developer tools (coding agents, Copilot-style assistants, AI code review, AI-driven test generation) with a clear view on where they help and where they don’t. 

Experience writing and reviewing AI-generated code with the same (or higher) rigor as human-written code. 

Comfort integrating LLMs, machine learning models, agent frameworks and evaluation tooling into backend services. 

Hands-on with CI/CD pipelines (GitHub Actions, GitLab CI, Azure DevOps) and modern practices for automated testing, code quality and safe deployment. 

Familiarity with DORA metrics and engineering analytics to measure the impact of changes in process and tooling. 

Excellent communication and problem-solving; a track record of delivering complex projects on time. 

Self-driven and proactive — you pick up new tools, form a view, and bring the team with you.