AI Specialist – LLM & Data Engineering

About Us

We are an AI-powered consumer intelligence company transforming qualitative research through cutting-edge automated moderation and real-time insight generation. Our platform is powered by advanced Large Language Models (LLMs), GenAI-driven analytics, and a modern cloud data ecosystem across AWS, Azure, and MongoDB.

The Role

You will design, refine, and productionise the AI systems that power our platform — from AI moderators and synthetic respondents to automated insight-generation models and evaluation frameworks. This is a hybrid role combining LLM development, finetuning, and AI evaluation with data engineering, cloud infrastructure, and lakehouse architecture.

What You’ll Do

LLM & GenAI Design + Finetuning

• Design prompts, reasoning flows, and intelligent behaviours for AI moderators and synthetic respondents.
• Fine-tune LLMs using curated datasets (LoRA/QLoRA, supervised finetuning, RLHF-style workflows).
• Prototype new GenAI features, including automated insight extraction, multi-turn conversation handling, and generative UX testing.
• Run structured experiments to optimise model performance, prompt strategies, hyperparameters, and inference configurations.

Data Engineering & Cloud Systems

• Build and maintain scalable data pipelines for model training, evaluation, analytics, and production workflows.
• Work across AWS Athena, Glue, S3 and Azure equivalents to support a lakehouse architecture.
• Use MongoDB effectively with strong schema design, indexing, and aggregation pipeline skills.
• Develop dbt models and SQL transformations for clean, reproducible datasets powering GenAI models.
• Build embedding pipelines, feature stores, and datasets for finetuning, retrieval, and evaluation.
• Ensure high-quality ETL/ELT workflows across AWS, Azure, and MongoDB environments.

Evaluation & Quality Systems

• Develop automated evaluation frameworks to measure LLM quality, accuracy, hallucinations, tone, and behavioural consistency.
• Build benchmark datasets and test suites for conversation quality, insight accuracy, and model reliability.
• Diagnose model failure modes such as drift, behaviour degradation, repetition loops, or hallucination patterns.
• Implement human-in-the-loop evaluation workflows for calibration and continuous improvement.

Cross-Functional Collaboration

• Work with engineering to deploy LLM-driven features and integrate evaluation into CI/CD pipelines.
• Partner with product and research teams to ensure AI behaviours align with qualitative research standards.
• Monitor model quality, data pipeline performance, and dataset integrity, driving iterative improvement.

Requirements

• 3+ years of experience in AI/ML engineering, LLM development, or applied data science.
• Hands-on experience finetuning LLMs (HuggingFace, LoRA/QLoRA, custom datasets, RLHF).
• Strong programming skills in Python and SQL.
• Solid experience with MongoDB including schema design, indexing, and aggregation pipelines.
• Experience with AWS (S3, Athena, Glue, Lambda, Step Functions) and Azure (ADLS, Data Factory, Azure ML).
• Understanding of data lakehouse architectures and modern data warehousing.
• Experience building dbt models and maintaining ETL/ELT workflows.
• Familiarity with embeddings, vector retrieval, evaluation frameworks, and model performance analysis.
• Experience building datasets and pipelines for LLM training, evaluation, or retrieval systems.

Bonus Points

• Experience with RAG pipelines, vector databases (Pinecone, Weaviate, OpenSearch), or multi-agent orchestration.
• Experience building conversational agents or AI-driven automation systems.
• Knowledge of MLOps tooling such as SageMaker, MLflow, or Databricks.
• Familiarity with RLHF, alignment techniques, or model interpretability.
• Interest in consumer behaviour, research methodologies, or product decision-making.
• Startup experience or comfort with high-velocity environments.

Why Work for Our Organization?

• Hybrid working — home or London office (UK work rights required).
• Private Medical Insurance via Vitality.
• Nest Pension contributions.
• Employee referral bonuses.
• Virtual and in-person team gatherings.
• Recognition awards for outstanding contributions.
• Annual bonus — up to two months’ salary based on company performance.
• A unique opportunity to shape the future of AI-driven qualitative research through advanced LLM and data engineering systems.