AI Engineer - Remote

Location: Brighton (Hybrid) / Europe (Remote) About Attercop Attercop is a knowledge engineering and agentic AI company built specifically for private equity and professional services. We turn fragmented, hard to use information into structured intelligence and deploy autonomous AI agents that make decisions, not just recommendations. Our systems deliver faster insights, operational efficiency gains, and real value creation across the PE lifecycle — from diligence and data organisation to portfolio wide transformation. Role Overview You’ll design, build, and integrate advanced AI models into real software systems. This role sits at the intersection of software engineering, data science, and MLOps — turning research into robust, scalable, production-ready AI services. Core Responsibilities Model Engineering Build functional AI services from architectural designs Orchestrate data ingestion, inference flows, and output pipelines Optimise latency, memory, and throughput Implement testing, validation, and error/bias analysis Agentic Workflows Design multi-agent systems using LangChain, LangGraph, or Microsoft Agent Framework Implement reasoning loops (e.g., ReAct) Integrate tools, APIs, databases, and memory systems Develop safety and reliability checks for agent behaviour Data Engineering Build scalable ETL/ELT pipelines Perform feature engineering and advanced data prep Integrate SQL/NoSQL, data lakes, warehouses, and streaming APIs Ensure compliance with GDPR/CCPA and internal governance MLOps \& Deployment Deploy AI services on Azure using REST APIs Use Docker + Kubernetes for scalable production workloads Build ML-focused CI/CD pipelines Implement monitoring, drift detection, logging, alerting, and retraining Manage infrastructure with Terraform Candidate Requirements Experience 2+ years as an AI Engineer or Software Engineer with strong AI/ML exposure Technical Skills Advanced Python (asyncio, type hinting, Pydantic) Backend/API development with FastAPI/Flask/Django Agentic frameworks (LangChain, LangGraph, Microsoft Agent Framework) LLM orchestration, RAG, prompt engineering Cloud (Azure preferred), Docker, Kubernetes IaC (Terraform) PostgreSQL, plus exposure to NoSQL and vector databases CI/CD, monitoring, observability, ML-specific drift detection Collaboration \& Communication Work effectively with data scientists, PMs, and stakeholders Communicate technical decisions clearly Maintain strong documentation across pipelines and architectures