• SaaS Feature Development: Architect, build and deploy AI applications and interactive features directly into the LAL platform to drive user engagement and customer value.

• Internal Process Optimisation: Identify operational bottlenecks and build high-precision automated pipelines, using appropriate AI and deterministic methodologies.

• AI Systems Architecture & Infrastructure: Design, test and maintain scalable agentic workflows that seamlessly integrate with LAL’s core infrastructure.

• Quality, Guardrails & Evaluation: Establish robust evaluation frameworks, hallucination tracking, prompt engineering standards, and fallback mechanisms to ensure high accuracy and compliance in non-deterministic model outputs.

• Technical Standards & Expansion Foundation: Document technical patterns, prompt structures, and deployment pipelines to establish a foundation that allows internal Developers or future hires to build on top of our AI architecture.

What We’re Looking For

Technical Skills & Experience:

• Building AI Applications: Hands-on experience building, testing and shipping real-world AI features into live software platforms (building applications that use AI models, rather than just using AI assistants to write standard code).

• Search & Knowledge Retrieval: Practical experience connecting AI models to company documents and databases so systems can search, retrieve and answer queries using accurate internal data.

• Document Processing & Data Extraction: Proven ability to build automated tools that read unstructured documents (PDFs, images, forms) and cleanly extract accurate information which can then be integrated down-stream into our application(s).

• Core Software Engineering: Strong background in standard full-stack  development using Java and TypeScript, with solid experience connecting and integrating agentic solutions with front and backend systems.

• AI Quality, Accuracy & Monitoring: Experience measuring AI accuracy, tracking usage costs, and building safety checks and fallback rules to prevent models from giving incorrect or fabricated answers.

• Data Security & Compliance: Basic understanding of data privacy laws (GDPR) and handling sensitive customer data safely, including AI related best practices, guidelines and regulations.

Required Competencies:

• Self-Direction & Ownership: Ability to take broad, high-level business problems from shaping to shipped production software without the need for supervision.

• Practical Product Focus: Strong focus on commercial outcomes, balancing technical elegance with user value, and prioritising rapid delivery and ROI over experimental projects.

• Smart Problem Solving: Ability to spot technical risks early, make sensible compromises when time is tight and keep projects moving forward smoothly.

• Technical Communication & Collaboration: Strong written and verbal communication skills to articulate AI architecture choices clearly to technical leadership and explain how AI systems work in simple terms to colleagues.

• Leadership & Mentorship: Willingness to establish clear coding standards, documentation and evaluation frameworks that empower internal team members to easily learn from,  maintain and build on your work as the team grows.

Life at LAL