AI Engineer – Life & Health Re (Advance with AI)

NextLink Group is embarking on an 'Advance with AI' journey to embed AI-driven intelligence across end-to-end Life & Health operations. The AI Engineer is responsible for engineering, operationalizing, and governing AI solutions in production, ensuring they are safe, scalable, and business-ready.

Responsibilities:

• Own and maintain prompt engineering strategies, including prompt versioning, testing, and optimization
• Design AI workflows that combine models, prompts, tools, enterprise data, and business logic
• Implement AI orchestration layers to manage multi-step reasoning, decisioning, and actions
• Ensure AI systems integrate cleanly into business workflows, APIs, and user interfaces
• Apply guardrails to ensure safe, explainable, and compliant AI behavior
• Support in building and maintaining production-grade deployment pipelines for AI solutions
• Ensure reliability, scalability, cost control, and latency optimization
• Implement monitoring and observability for AI systems (usage, performance, drift, failures)
• Define and enforce change control, versioning, rollback, and release management processes
• Collaborate closely with data scientists, actuaries and other business functions
• Validate model behavior, outputs, and assumptions from a production and business-use perspective
• Communicate engineering limitations and operational considerations to stakeholders

Requirements:

• 5+ years of experience
• 2+ years hands-on experience building, calibrating and operationalizing AI/ML solutions
• Exposure to Data Science and model development (collaboration, implementation, validation), without requiring deep Data Scientist specialization
• Strong algorithmic and problem-solving skills
• Strong programming skills in Python/PySpark and strong SQL expertise
• Exposure to Data Science methods in validating AI models
• Palantir Foundry and AIP experience
• Hands-on experience with prompt engineering, prompt testing, and prompt lifecycle management
• Experience implementing RAG architectures and similar approaches
• Experience with AI orchestration frameworks, agentic patterns, and tool/function calling
• Strong understanding of model evaluation, calibration techniques, and monitoring
• Familiarity with model explainability, fairness, and robustness
• Experience with MLOps tooling and practices
• Experience working in cloud environments (AWS, Azure, or GCP)
• Experience integrating AI models into production systems with monitoring, logging, and alerting
• Experience working with large data sets on enterprise data platforms and distributed computing (Spark/Hive/Hadoop preferred)
• 1–2 years exposure to insurance or reinsurance
• Experience with agentic AI frameworks and enterprise-scale AI platforms
• Experience working in insurance/reinsurance (e.g. Underwriting, Claims, Inforce management for L&H)