AI Engineer - Platform

Jubilee Insurance is seeking a highly motivated AI Engineer – Platform to join their dynamic team in Nairobi. This entry-level role offers an exciting opportunity to build and optimize scalable AI infrastructure, with a focus on Vertex AI Agent Builder, and drive the development of innovative AI solutions across the company. If you’re a young Kenyan professional with a desire to shape the future of AI within the insurance industry, Jubilee Insurance encourages you to apply.

Jubilee Insurance was established in August 1937, as the first locally incorporated Insurance Company based in Mombasa. Jubilee Insurance has spread its sphere of influence throughout the region to become the largest Composite insurer in East Africa, handling Life, Pensions, General and Medical Insurance. Today, Jubilee is the number one insurer in East Africa with over 450,000 clients. Jubilee Insurance has a network of offices in Kenya, Uganda, Tanzania, Burundi. It is the only ISO certified insurance group listed 2on the three East Africa stock exchanges The Nairobi Securities Exchange (NSE), Dar es Salaam Stock Exchange and Uganda Securities Exchange. Its regional offices are highly rated on leadership, quality and risk management and have been awarded an AA- in Kenya and Uganda, and an A+ in Tanzania. For more information, visit www.JubileeInsurance.com.

We currently have an exciting career opportunity for an AI Engineer – Platform within Jubilee Life Insurance Limited. The position holder will report to the Head of Data and will be based at our Main Office.

Role Purpose

The role holder is responsible for building, optimizing, and maintaining scalable AI infrastructure and systems that enable the efficient development, deployment, and operation of AI solutions across Jubilee, with a primary focus on Vertex AI Agent Builder. The role focuses on implementing end-to-end AI features, enhancing model performance, and managing distributed training and deployment environments, while ensuring seamless integration of AI models into production systems. Working within a fast-paced, product-driven environment, the role combines strong machine learning fundamentals with infrastructure expertise to deliver robust, high-performing AI capabilities that support business innovation and rapid delivery of AI-powered solutions.

Main Responsibilities

Strategy

1. Contribute to strategic decisions on AI architecture, including selection between LLM approaches such as fine-tuning, API-based models, or RAG solutions.
2. Evaluate emerging AI/ML technologies and recommend adoption aligned to business and platform needs.
3. Provide technical input into product and platform roadmaps, ensuring scalability and performance considerations are embedded early.
4. Drive continuous improvement through innovation, experimentation, and optimization of AI infrastructure and systems.

Operational

1. Design, develop, and deploy end-to-end AI features using Vertex AI Agent Builder and related Google Cloud services.
2. Build and maintain scalable ML pipelines, deployment infrastructure, and monitoring systems.
3. Optimize model performance through techniques such as TPU optimization, distributed training, and inference tuning.
4. Implement LLM post-training techniques including fine-tuning, RLHF, prompt engineering, and RAG architectures.
5. Develop and optimize custom CUDA kernels and GPU-based solutions to improve efficiency and performance.
6. Own backend systems, model integration, and API development to support AI-powered products.
7. Conduct performance profiling and optimization of AI models in production environments.

Corporate Governance

1. Ensure adherence to engineering best practices, coding standards, and MLOps frameworks.
2. Maintain high-quality technical documentation including architecture designs, API specifications, and deployment guides.
3. Participate in code reviews to uphold code quality, maintainability, and security standards.
4. Identify and mitigate technical risks related to performance, scalability, and system reliability.

People and Culture

1. Collaborate effectively with cross-functional teams including engineering, product, and business stakeholders.
2. Contribute to a strong engineering culture through knowledge sharing, peer reviews, and collaborative problem-solving.
3. Demonstrate ownership and accountability for end-to-end delivery of Al features..
4. Support continuous learning and adoption of best practices within the team.

Key Competencies

1. Strong analytical and problem-solving capability in complex Al systems.
2. Deep understanding of machine learning fundamentals and model behavior.
3. Ability to work independently and take ownership of end-to-end delivery.
4. Strong communication skills for both technical and non-technical audiences.
5. Strategic thinking to align technical solutions with business objectives.
6. Adaptability and ability to thrive in fast-paced, evolving environments.
7. Strong collaboration skills within cross-functional teams.

Academic Qualifications and Relevant Experience

1. Bachelor’s degree in Computer Science, Software Engineering, Mathematics, or a related field.
2. Relevant certifications in Google Cloud Platform, Al, or Machine Learning are an added advantage.
3. Demonstrated portfolio of AI/ML projects (e.g., GitHub, research, or production systems) is beneficial.
4. 2-3 years of experience in machine learning engineering or Al development.
5. Proven experience deploying ML models into production environments.
6. Hands-on experience with Google Cloud Platform and Vertex Al.
7. Experience with distributed training, TPU/GPU optimization, and LLM-based applications.
8. Experience working in agile, fast-paced environments such as startups or mid-sized technology organizations is preferred.
9. Exposure to customer-facing Al products or production-grade systems is advantageous.