Summary: This senior backend engineering role involves shaping the core architecture and development of conversational AI and digital human platforms, focusing on advanced systems and cutting-edge technologies. Highlights: 1. Build and scale backend systems for conversational AI and digital humans 2. Hands-on senior role balancing architecture with production code 3. Work deeply with LLMs, RAG pipelines, and real-time systems Senior Backend Engineer (LLM / AI Experience)**Hybrid · Tech Team · Full\-time** Barcelona, Spain In a few words* Build and scale **backend systems powering conversational AI \& digital humans** * Hands\-on senior role balancing **architecture \+ production code** * Work deeply with **LLMs, RAG pipelines, and real\-time systems** * Barcelona (hybrid) or remote in Europe \| €50k–€65k **Why this role is exciting:** You’ll shape the backend foundations of a cutting\-edge digital human platform, where your architectural and performance decisions directly impact real\-time AI experiences used by enterprise customers. About UNITH At **UNITH**, we’re transforming customer journeys with conversational AI. Listed on the ASX, we create **lifelike digital humans** using cutting\-edge synthetic facial movement, voice engineering, and conversational design. Our digital humans speak **60\+ languages** with **600\+ voices**, redefining how businesses interact with customers worldwide. The Role We’re looking for a **Senior Backend Engineer with recent LLM product experience** who combines strong architectural thinking with hands\-on development. You’ll work closely with the **Head of Engineering**, playing a key role in technical decisions while remaining deeply involved in day\-to\-day coding, feature delivery, and system optimization. You’ll be a senior technical voice on the team — someone who designs systems and builds them. ️ What You’ll DoArchitecture \& System Operations (50%)* Actively participate in architectural decisions with the Head of Engineering * Collaborate with the Cloud Infrastructure Engineer on platform architecture, observability (monitoring, logging, alerting), and deployment strategies * Design and optimize systems: performance profiling, database queries, caching, and resource usage * Own production operations: troubleshooting, incident response, and on\-call * Provide technical guidance through code reviews, design discussions, and best practices * Collaborate on real\-time streaming architecture with the Video Synthesis Engineer Feature Development \& Implementation (50%)* Implement backend features and services in production\-grade **Python and Go** * Build conversation features: state management, history, and intelligence improvements * Implement multi\-document knowledge bases using **AWS Bedrock** * Integrate LLM APIs (OpenAI, AWS Bedrock) and build **RAG pipelines** * Develop APIs and service integrations (gRPC, REST) * Work on core backend services: orchestration, caching, and platform APIs * Own testing, CI/CD pipelines, and deployment automation Tech Stack* Python (FastAPI) and Go (gRPC services) * AWS (S3, EC2, Lambda, Bedrock, managed services) * Docker, Kubernetes, RabbitMQ, Redis * LLM APIs (OpenAI, AWS Bedrock) ✅ What We’re Looking ForMust\-Have* 5\+ years of backend engineering experience with distributed systems, microservices, and real\-time architectures (WebSocket, gRPC, event\-driven) * Experience building and deploying complex, highly\-performant Python applications * 2\+ years building **LLM\-powered products in production** (2022–2025\), including hands\-on experience with LLM APIs (OpenAI, Anthropic, AWS Bedrock) and RAG systems * Comfortable balancing architecture design with hands\-on implementation * Strong AWS experience with focus on performance optimization, observability, and production operations * Proven ability to optimize production systems (latency, throughput, technical debt) * Excellent collaboration skills across backend, infrastructure, and AI/ML teams Bonus Points* Golang experience * Experience with video streaming, media processing, or conversational AI platforms * Data engineering or ML model serving infrastructure experience What Success Looks Like **First 6 months** * Knowledge transfer completed and ownership of critical backend services established * Multi\-document knowledge base and conversation features live in production * Active contributor to architecture discussions with measurable performance improvements * Production systems well\-monitored with improved observability **First 12 months** * Core backend services and RAG pipeline running reliably in production * Platform\-wide performance optimizations delivered (Q1–Q3 targets met) * Backend engineers unblocked and supported through your technical guidance * Recognized as the go\-to expert for backend \+ LLM implementation What We OfferCompensation \& Flexibility* Salary: **€50,000 – €65,000**, depending on experience * Hybrid work in Barcelona or remote options within Europe Impact \& Growth* Ownership of critical backend services used daily by enterprise customers * Hands\-on technical work alongside architectural responsibility * Deep technical challenges across LLMs, RAG pipelines, real\-time systems, and scalability * High\-impact role in a **small, senior team (12 people)** * Close collaboration with Engineering, AI research, and infrastructure teams * Opportunity to build expertise in the fast\-evolving digital humans domain Additional Perks ️ Office in the center of Barcelona Work from anywhere ️ Lunch compensation when in the office Private health insurance with Alan Travel allowance (for team members living 10km\+ from the office) Flexible benefits (tax\-free under Spanish legislation) ️ ClassPass discount How to Apply: Submit: * Your CV highlighting **ML production experience** * A short motivation (3–5 sentences) covering: * Backend systems you’ve built and maintained * LLM features you’ve implemented in production * A performance optimization project you worked on * Your experience with RAG or knowledge bases * Why digital humans excite you Apply via the **Easy Apply** button, or reach out directly to **joyce@unith.ai** — creativity is welcome Recruitment Process 1\. Technical interview with the tech team (60 min) 2\. Small take\-home assignment 3\. Assignment review (60 min) 4\. Reference check 5\. Call with Joyce (30 min) * ️ **Timeline:** 2–3 weeks from application to offer **Ready to make digital humans faster, better, and more reliable?** Apply now