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As an AI Engineer, the Research Engineer builds the systems that bring AI agents to life and scales the infrastructure that powers LLM-driven synthetic populations. The role designs agent cognitive architectures, implements 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 frameworks for building AI / LLM applications (e.g. PyTorch, Hugging Face Transformers, LangChain)
* 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
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Disclaimer: Calyptus uses an automated assessment tool that scores applicants.
Want to put your job search on autopilot? Join our platform, complete a 6-minute AI screening interview, and get auto-applied to 100s of high-paying roles.
Sign up now at https://app.calyptus.co/auth/candidate/sign-up and let the opportunities come to you.