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)