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# AI Engineer

- **Company:** Epistemix
- **Location:** Epsom, United Kingdom (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Validation, Serialization, Database Models, Machine Learning, Performance Tuning, SQLAlchemy, Systems Architecture, AI Infrastructure, Pinecone, Large Language Models, Prompt Engineering, Fastapi, Information Technology, Modeling and Simulation, Weaviate, Machine Learning Operations, FAISS, Virtual Agents, Evaluation of Large Language Models, Restful APIs, Docker
- **Published:** November 14, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/ai-engineer/56430232

## About the Role

QualificationsBachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field (or equivalent experience).
3+ years of experience developing AI/ML applications in production environments.
Proven track record of working with LLMs, NLP models, or AI-driven systems.
Experience designing and optimizing high-performance, scalable APIs.
Strong problem-solving skills and ability to work in a fast-paced environment.
Must be legally authorized to work in the United States and not require employer sponsorship now or in the future.

Required SkillsPython - Advanced proficiency in writing clean, efficient, and scalable code.
Pydantic - Strong experience in data validation, serialization, and structured model definition.
LLM Evaluation - Ability to assess model performance, optimize outputs, and fine-tune AI behavior.
Prompt Optimization - Expertise in crafting, refining, and iterating prompts for optimal AI performance.
SQLAlchemy - Hands-on experience with database modeling, ORM techniques, and performance tuning.
FastAPI - Proven ability to develop and maintain APIs with FastAPI for AI-driven applications.

Nice To Have ExperienceExperience with vector databases (e.g., Pinecone, Weaviate, FAISS) for efficient AI retrieval.
Familiarity with Docker & Kubernetes for containerized AI application deployment.
Knowledge of cloud platforms (AWS, GCP, or Azure) for scaling AI infrastructure.
Understanding of retrieval-augmented generation (RAG) techniques.
Background in MLOps practices for automating AI model deployment and monitoring.

## Description

The Full Stack AI Engineer plays a critical role in making modeling and simulation accessible to non-data scientists. You will design, develop, and deploy AI-driven applications to extend the reach of our software, directly impacting the number of organizations we serve. This position is pivotal to our product roadmap and contributes to the company's success and growth. Ideal candidates exhibit a high willingness to experiment and empathy for users.
About EpistemixEpistemix helps organizations forecast outcomes and manage risk by modeling how people behave. Our platform consists of two core products: Populus, which provides access to realistic, high-resolution synthetic population data, and Polaris, which enables scenario planning through advanced data science and simulation. Together, they empower customers to evaluate the potential impact of strategies before deploying them in the real world.
Organizations across healthcare, consumer industries, insurance, and government use Epistemix to reduce uncertainty, optimize decisions, and accelerate time to value. Whether estimating total addressable market, testing public health interventions, or forecasting behavior change, our tools help teams make confident decisions when pre-existing data may not exist.
Leading up to our Series B financing, we are hiring key roles onto the team and entering an exciting phase of growth and innovation. Join us to combine cutting-edge technology with a purpose-driven mission to make the world a better place.
ResponsibilitiesCraft clean, testable, and maintainable code to enable AI-generated agent-based models.
Own the software from requirements development through deployment and maintenance to enable decision makers to generate agent-based models, and help data scientists build models faster.
Design, build, test, and deploy a scalable system architecture so AI-generated models can be validated by data scientists and deliver results back to decision makers quickly.
Own the engineering solution and collaborate with internal teams to ensure alignment with company strategy.