Software Engineer

Attercop is a specialist AI company working with private equity firms and their portfolio companies. We combine advisory work (AI strategy, governance, technical due diligence) with hands-on delivery (data science, AI engineering, and full-stack software). Our delivery team takes proofs of concept through to production-grade systems, working directly with clients on fast-paced, outcome-focused engagements. We operate an AI-first development environment built around specification-led design and test-driven delivery.

Every engagement is run as a disciplined software project, with clear acceptance criteria, rigorous QA, and the engineering hygiene that holds up under the scrutiny of institutional clients.

Role Overview

This is a hands-on senior engineering role in a small, multi-disciplinary team. You will lead the technical delivery of client engagements, work alongside data scientists, AI engineers, and project managers, and take responsibility for the quality, reliability, and maintainability of the systems we build. You will move between projects as priorities shift, and contribute meaningfully across the full software development lifecycle, from scoping and specification through to deployment and handover.

Core Responsibilities

• Software Delivery in a Fast-Paced Environment: Take ownership of technical delivery within client engagements, translating specifications into working systems that meet agreed acceptance criteria. Collaborate with project managers, data scientists, and AI engineers to scope work, estimate effort, and track progress against commercial timelines. Manage the tension between pace and quality that is characteristic of agency work, shipping reliably on short timescales without accruing the kind of technical debt that undermines long-term client outcomes. Contribute to engagement planning, risk identification, and client-facing technical communication.
• Specification-Led and Test-Driven Development: Work from written specifications and design documents, and contribute to authoring them where needed. Apply test-driven development as a first-class practice (unit, integration, and end-to-end), with coverage expectations appropriate to the risk profile of the system. Implement QA processes that catch defects early, including automated testing, code review, and structured validation against specifications. Maintain traceability between requirements, implementation, and tests.
• CI/CD and DevOps: Design, build, and maintain CI/CD pipelines using GitHub Actions or Azure DevOps, covering build, test, security scanning, and deployment. Establish and enforce branching strategies, pull request workflows, and release processes appropriate for the engagement. Implement observability from the outset: structured logging, metrics, tracing, and alerting that give both the team and the client confidence in production systems. Contribute to infrastructure provisioning using Infrastructure as Code (Terraform preferred).
• Backend Services Design and Development: Design and build backend services, APIs, and data pipelines to production standards. Apply software design patterns, Clean Architecture principles, and a clear view on modularity, testability, and long-term maintainability. Contribute to architectural decisions across the team, documenting trade-offs and reasoning. Mentor less senior engineers through code review, pairing, and technical guidance.
• AI-First Development Practices: Work effectively in an AI-first development environment, including fluent use of AI-assisted coding tools and agentic development workflows. Partner with data scientists and AI engineers to integrate models, orchestration logic, and retrieval systems into production services. Develop AI orchestration skills on the job: deep expertise is not required from day one, but active engagement with the AI engineering side of the team is.

Candidate Requirements

Professional Experience: Minimum of 4 years of professional software engineering experience, with demonstrable seniority in at least one commercial setting. Experience in a software development agency or similar client-services environment is strongly preferred, or equivalent experience of shipping software under fixed timelines and external scrutiny. Track record of owning delivery of non-trivial systems from specification through to production. Strong Python skills, with deep experience in asynchronous programming (asyncio), parallel processing (celery), and managing high-throughput backend workloads. Proven ability to implement and deploy distributed architectures where parallel and asynchronous processing is essential. Production experience building robust APIs (FastAPI preferred). Capable of designing systems that handle external constraints such as LLM API rate limits (TPM/RPM) and throttling. Solid command of software design patterns, Clean Architecture, and the ability to identify and mitigate technical, security, and operational risks.

Software Engineering Practices: Deep familiarity with Git-based workflows, code review culture, and modern branching strategies. Proven experience with test-driven development and a range of testing approaches (unit, integration, contract, end-to-end). Demonstrable experience contributing to specification-led delivery processes. Hands-on experience with Azure (preferred) or AWS, including container orchestration (AKS, EKS), managed container services, and serverless compute. Proficiency with message brokers and processing queue services (RabbitMQ, Kafka, or cloud-native equivalents) for asynchronous task execution. Production experience with Docker and Kubernetes. Working knowledge of Terraform or equivalent IaC tooling for infrastructure provisioning. Demonstrated ability to build and maintain CI/CD pipelines using GitHub Actions or Azure DevOps. Expertise in operationalising distributed systems, including log aggregation, distributed tracing, APM, infrastructure metrics and observability practices. Comfort with security hygiene in pipelines (secrets management, dependency scanning, basic supply-chain awareness). Proficiency with relational databases (PostgreSQL preferred), including schema design and query performance. Familiarity with NoSQL and vector databases is a plus, but can be developed in the role. Exposure to LLM APIs, RAG architectures, or agentic frameworks (LangChain, LangGraph, Microsoft Agent Framework) is welcome. More important is a genuine interest in AI-first engineering practices and a willingness to develop these skills alongside our specialist team.

Strategic and Collaborative Competencies: Agency Mindset: Comfort working across multiple concurrent engagements, managing competing priorities, and communicating directly with clients. Cross-Functional Collaboration: Ability to partner with data scientists, AI engineers, project managers, and commercial leads to define scope and deliver outcomes. Technical Communication: Capability to articulate architectural decisions, trade-offs, and delivery risks to both technical peers and non-technical client stakeholders. Documentation Discipline: Commitment to maintaining clear documentation for architectures, pipelines, test strategies, and deployment protocols. Mentorship: Willingness to support the growth of less senior engineers through code review, pairing, and technical coaching.

All roles require excellent English. We work entirely in English for meetings, client calls, and business communications. This is non-negotiable.