AI Engineer

Caava Group AI Engineer Role

Role Description

Caava Group is a holding company at the forefront of transforming the African insurance landscape. It brings together multiple complementary businesses under one umbrella — Caava Evolve, Caava AI, Agencify, and TurnQuest V4 by Caava — modernizing legacy insurer operations, expanding digital distribution through agents, and harnessing AI-driven solutions to streamline underwriting, claims, and customer engagement.

We are recruiting to fill the position below:

Job Title: AI Engineer

Location: Lagos (Hybrid)

Reporting to: Technical Lead of the assigned Evolve team

Role Purpose

• Evolve is Caava Group’s insurance management platform, built on Spring Boot services with Angular and Next.js frontends, and used by AIICO for its insurance operations.
• This role helps build, test and maintain AI capabilities used across Evolve teams. Working with experienced engineers, you will use Python to prepare data, integrate approved model and LLM APIs with Evolve services, and evaluate whether AI features behave reliably for their intended use.
• This entry-level role focuses on practical implementation and evaluation. It suits candidates with internship, academic or personal project experience who learn quickly, document their work and ask for guidance when needed.

Career Path

• Progression to Associate AI Engineer depends on consistent delivery against these measures, growing independence on larger tasks, code and test quality, and business need.

Additional Information

• Works With: Software Engineers, QA Engineers and Product across Evolve teams
• Scope: AI-enabled features across Evolve: data preparation, model/LLM integration, evaluation and monitoring support
• Tech Stack: Python, REST APIs, model and LLM APIs, Git and evaluation tools; integration with Evolve’s Spring Boot services
• Growth Path: Associate AI Engineer, based on performance and business need

Core Accountabilities

Build AI components and prepare data:

• Implement clearly scoped Python components, scripts and integrations to agreed designs, exposed to Evolve services through agreed APIs. Add input validation, error handling and tests with guidance.
• Prepare and validate approved datasets for development and evaluation. Check formats, missing values and labelling quality; document transformations and limitations.

Integrate models and evaluate outputs:

• Integrate model or LLM APIs into Evolve workflows. Support prompt configuration, structured outputs and response validation using approved tools and data.
• Create and run repeatable evaluation cases covering typical, edge and failure scenarios. Compare outputs against agreed criteria and document errors, limitations and regressions.

Investigate failures and handle data safely:

• Help investigate unreliable outputs and integration failures. Record findings and escalate quality, safety or data concerns to experienced engineers.
• Apply approved privacy and security practices. Keep personal data, credentials and confidential information out of unapproved prompts, datasets, logs and external services.

Support monitoring and engineering practice:

• Support deployment and monitoring under guidance. Check API failures, response time, usage and cost, and help implement agreed fallback or human-review steps.
• Use Git, tests and code reviews to keep code readable. Document APIs and evaluation results, apply feedback and communicate progress and blockers early.

Requirements

Experience and Working Style:

• A Degree in Computer Science, Software Engineering, Data Science or a related field, or equivalent practical training supported by relevant projects.
• Foundational Python skills shown through coursework, an internship or a working project.
• Basic understanding of machine learning concepts, evaluation metrics and the limits of model outputs, including inaccurate or unsupported responses.
• Familiarity with REST APIs, HTTP and JSON, Git, automated tests and debugging in an existing codebase.
• Ability to work with structured data, use documentation and explain the approach and results of a technical task.

Working Style:

• Attention to detail and sound judgement about handling data.
• Clear communication and a willingness to seek and apply feedback.

Nice to Have:

• Exposure to FastAPI or Flask, and Python data tools such as pandas or NumPy.
• A project using a model API, LLM, scikit-learn, PyTorch or TensorFlow; experience with every framework is not expected.
• Familiarity with SQL, Docker, Java/Spring Boot, cloud concepts or CI workflows, and interest in practical insurance uses of AI.

Application Closing Date

Not Specified.