Founding AI Engineer
Location : London (Hybrid / Flexible)
Salary : Competitive DOE + Bonus + Equity
Company Overview
We are working with an early-stage technology company building AI-native systems that help large organisations better understand, govern, and secure complex data and identity ecosystems.
Operating at the intersection of AI, cyber security, and enterprise data platforms , the business is focused on transforming how organisations model access, risk, and data relationships across increasingly distributed cloud environments.
The team is small, highly technical, and product-focused, with a strong bias toward ownership, rapid iteration, and building systems that operate in complex, real-world enterprise environments. Engineering sits at the centre of everything, and individuals are expected to have a direct impact on product direction and architecture.
The Role
We are seeking a Founding AI Engineer / Engineering Lead to join the business at a pivotal stage as the platform moves from early MVP into scalable production use with enterprise customers.
This is a hands-on technical leadership role , not a product management position.
You will be responsible for designing, building, and scaling core components of an AI-driven enterprise platform, working across data, machine learning, and distributed systems. You will also work directly with early customers to understand real-world use cases and translate them into robust, scalable product capabilities.
This role suits someone who still enjoys building and shipping code, but is comfortable influencing architecture, mentoring engineers, and shaping technical direction.
You will have significant influence over:
Core system architecture
AI/ML strategy and implementation
Data modelling and enterprise integration patterns
Early product design decisions
Engineering standards and scalability approach
Key Responsibilities
Design and build core components of an AI-native enterprise platform from the ground up
Develop and deploy machine learning and LLM-based systems in production environments
Build scalable data and AI pipelines across complex enterprise datasets
Work closely with early enterprise customers to refine requirements and shape product direction
Define and implement system architecture for AI, data, and integration layers
Build agentic AI workflows and knowledge-driven systems using modern LLM frameworks
Implement robust MLOps pipelines for model training, deployment, monitoring, and governance
Contribute directly to production code in Python and related AI/data tooling
Support technical decision-making around cloud architecture, scalability, and system design
Help establish engineering best practices in a fast-moving early-stage environment
Tech Stack
Python
TensorFlow / PyTorch
LLM frameworks (LangChain, LangGraph, or similar agentic AI tooling)
AWS / Azure cloud platforms (including AI services such as Bedrock)
Data engineering tools (e.g. AWS Glue or equivalent)
NLP, embeddings, vector databases, RAG architectures
MLOps tooling for model deployment and monitoring
Experience with distributed data systems and APIs
Required Skills & Experience
Several years' experience in software engineering, data engineering, or AI engineering roles
Strong academic background in Computer Science, Engineering, Mathematics, or similar
Experience working in a product-led technology environment (SaaS, AI platform, or similar)
Proven ability to ship production-grade AI or data systems
Strong Python engineering capability
Experience with modern ML / LLM systems and frameworks
Exposure to cloud-based AI/ML platforms (AWS, Azure, or GCP)
Comfortable working in early-stage environments with ambiguity and ownership
Strong communication skills and ability to work directly with technical and non-technical stakeholders
Bonus Experience (Highly Valued)
Cyber security / identity / access management (IAM / IdAM) exposure
Cloud operations, DevSecOps, or infrastructure-heavy environments
Experience in regulated or enterprise-scale domains
Knowledge of enterprise data governance, data modelling, or integration architecture
How You Work
Highly hands-on and enjoys building systems end-to-end
Comfortable operating in ambiguity and shaping problems as well as solving them
Strong bias toward delivery, iteration, and practical engineering outcomes
Collaborative, but able to take full ownership of technical decisions
Interested in building foundational systems, not just features
What We Offer
Highly competitive base salary (DOE) + bonus
Equity / stock options in a high-growth early-stage business
Private medical insurance
Hybrid working (London-based, flexible model)
High ownership role with significant influence on product and architecture
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