AI Engineer

About Electric Twin

Electric Twin is pioneering the future of behavioral intelligence using AI. We've built synthetic populations - digital twins of real audiences - that enable organisations to understand human behavior at unprecedented speed and scale. Our platform replaces weeks of traditional market research with seconds of AI-powered simulation, achieving 92% accuracy validated against real-world data.

Founded by Ben Warner (former Chief Data Advisor to the UK Prime Minister, architect of the 2019 election model) and Alex Cooper (19-year British Army veteran, Director of UK mass COVID testing), we serve leading enterprises and government agencies making high-stakes decisions. We're a seed-stage company backed by top investors, with a world-class team combining expertise in AI, behavioral science, and strategic simulation.

The Role

As an AI Engineer, you'll build the systems that bring our AI agents to life and scale the infrastructure that powers our LLM-driven synthetic populations. You'll design agent cognitive architectures, implement context engineering and memory systems, while ensuring these AI systems can operate reliably at scale in production environments.

This role balances AI agent development with backend engineering—ideal for engineers who want to work directly with large language models to create realistic behavioral simulations, while building the robust infrastructure needed to deploy them in enterprise settings.

What You'll Do

• Architecture & Development : Design and implement the cognitive systems that give AI agents consistent personalities, memory, and reasoning capabilities, using advanced LLM techniques like chain-of-thought prompting, RAG systems, and agentic tool use.
• Modeling & Experimentation : Design and run systematic experiments to evaluate agent behavior, test hypotheses about behavioral patterns, and iterate on model architectures based on empirical results and validation against real-world data
• LLM Engineering : Build sophisticated prompting strategies, behavioral frameworks, and decision-making systems that enable agents to exhibit realistic human-like behavior across diverse scenarios and demographics.
• Scalable Infrastructure & Optimization : Architect and deploy backend services that orchestrate large-scale agent simulations, balancing behavioral sophistication with computational efficiency while optimising prompt design, inference costs, and system performance as agent populations scale.

Who You Are

Essential Qualifications

• Bachelor's or Master's degree in CS, Math, Physics, AI, or related technical field
• Over 7 years of experience with at least 1 year working hands-on with large language models to solve complex problems
• Strong foundation in both AI/ML concepts and backend engineering principles
• Experience working in fast-paced environments where requirements evolve rapidly

Technical Skills

• LLM & Agent Development : Hands-on experience building applications with large language models, implementing advanced prompting techniques, RAG systems, and agentic workflows
• Backend Engineering : Proficient in Python and backend frameworks (e.g. FastAPI, Django, Flask); understanding of distributed systems and scalable architectures
• AI/ML Frameworks : Experience with PyTorch, Hugging Face Transformers, LangChain, or similar frameworks for building LLM applications
• Data & Storage Systems : Comfortable with databases (PostgreSQL, MySQL), vector databases, and embedding systems for retrieval
• Infrastructure : Knowledge of cloud platforms, containerisation, and deploying ML workloads to production

Desirable Experience

• Exposure to research-driven product development or academic AI research
• Experience with multi-agent systems, simulation frameworks, or agent-based modeling
• Knowledge of fine-tuning workflows, model optimization, and experiment tracking
• Understanding of statistical validation and data quality assessment

Personal Attributes

• Strong ownership mentality—you see projects through from design to deployment
• Pragmatic problem-solver who balances technical elegance with business needs
• Clear communicator who can explain complex technical decisions to non-technical stakeholders
• Thrives in ambiguity and adapts quickly as product requirements evolve
• Passionate about building infrastructure that enables innovative AI applications
• Intellectually honest—willing to question prevailing approaches and advocate for better solutions when evidence supports it
• Collaborative mindset—debates ideas vigorously while respecting other perspectives