Vacancies at Jubilee Insurance

Job Ref. No. JLIL392

Role Purpose

• The AI Engineer II (Solutions Engineer) is a senior-level technical role responsible for leading the design, development, and deployment of AI-powered solutions that address business challenges and enhance Jubilee’s digital platforms. This role combines deep machine learning expertise with strong fullstack engineering capabilities to deliver end-to-end AI features, integrate AI models into production systems, and ensure business-aligned outcomes. The incumbent provides technical leadership, mentors junior engineers, and makes strategic architectural decisions to ensure scalable, reliable, and high-impact AI solutions

Academic Qualifications and Relevant Experience

• Bachelor’s degree in Computer Science, Software Engineering, AI, Machine Learning, or related field (required).
• Master's degree or Ph.D. in Machine Learning, Artificial Intelligence, Computer Science, or related field (strongly preferred)
• Relevant AI/ML, cloud, or fullstack certifications are a plus.
• 4–7 years in AI/ML engineering or software engineering roles, with at least 2 years in a senior or lead capacity.
• Proven track record delivering production AI solutions end-to-end.
• Experience leading cross-functional technical projects and mentoring engineers.
• Hands-on experience with LLM-based solutions and modern AI frameworks.
• Background in integrating AI into customer-facing or operational products.
• Experience in agile, product-driven environments with iterative delivery cycles.

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Job Ref. No. JLIL389

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.

Academic Qualifications and Relevant Experience

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

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Job Ref. No. JLIL390

Role Purpose

• The Role holder is responsible for designing, developing, and deploying end-to-end AI-powered features across both backend systems and user-facing applications, ensuring seamless integration of intelligent capabilities into Jubilee’s digital platforms. The role combines strong machine learning expertise with fullstack engineering skills to deliver scalable, high-performing AI solutions that enhance customer experience, automate workflows, and support data-driven decision-making within a fast-paced, product-driven environment.

Academic Qualifications and Relevant Experience

• Bachelor’s degree in Computer Science, Software Engineering, Mathematics, or a related field.
• Relevant certifications in AI, cloud platforms, or software engineering are an added advantage.
• Demonstrated portfolio of fullstack or AI-driven projects (e.g., GitHub, deployed applications) is beneficial.
• 2–3 years of experience in AI engineering, software engineering, or fullstack development.
• Proven experience building and deploying end-to-end applications with AI components.
• Experience integrating AI/ML models into production systems and user-facing platforms.
• Experience working in agile, product-driven environments.
• Exposure to LLM-based applications and modern AI tools is advantageous.
   

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Job Ref. No. JLIL392

Role Purpose

• The role holder is responsible for operationalizing AI solutions, integrating AI models into production systems, and driving AI initiatives end-to-end with focus on business value delivery. The role combines technical expertise in machine learning, MLOps, and solution engineering with strategic thinking and team collaboration to ensure AI systems are reliable, scalable, and impactful. The ideal candidate has experience deploying AI/ML models, managing ML pipelines, and providing solutions that meet enterprise needs.

Academic Qualifications and Relevant Experience

• Bachelor’s degree in Computer Science, AI, ML, Software Engineering, or related technical field (required).
• Master’s or PhD in AI, ML, or Computer Science (preferred).
• Relevant certifications in cloud AI, ML, or MLOps (advantageous).
• 4–7 years of hands-on AI/ML engineering experience, with at least 2 years in senior or lead roles.
• Proven experience operationalizing AI solutions in production environments.
• Experience implementing MLOps best practices and managing scalable AI workflows.
• Background in LLM-based solutions, RAG systems, or AI Ops in enterprise contexts.
• Previous experience mentoring junior engineers and leading cross-functional AI projects.

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Job Ref. No. JLIL385

Role Purpose

• The Junior AI Engineer Intern will support the AI & Machine Learning team in developing, testing, and deploying AI solutions using Google Cloud Platform's Vertex AI. This role provides hands-on experience in building production-ready AI systems, working with large language models, and contributing to realworld insurance technology projects. The intern will learn industry best practices in MLOps, model deployment, and AI engineering while working under the guidance of senior engineers.

Academic Qualifications and Relevant Experience

• 0-1 year of experience (internships, academic projects, or personal projects)
• Academic projects involving machine learning or data analysis
• Personal projects demonstrating AI/ML skills (GitHub portfolio preferred)
• Previous internship experience in software development or data science (advantageous)
• Participation in hackathons, coding competitions, or AI challenges (preferred)
• Contributions to open-source projects (advantageous)
• Experience with Google Cloud Platform or other cloud services (preferred but not required)
• Demonstrated interest in AI through online courses, certifications, or self-learning
• Any exposure to insurance, healthcare, or financial services domain (advantageous)