Senior AI Engineer

At MB Life, we believe that AI is the core driver of breakthroughs in the insurance industry and a key enabler for optimizing the way we work. As a Senior AI/ML Engineer, you will be at the forefront of exploring, evaluating, and applying cutting-edge AI technologies from the open-source community and leading global providers to solve real-world business challenges.
Your mission is not to build everything from scratch, but to stand on the shoulders of giants-leveraging the power of pre-trained models, cloud-based AI platforms, and open-source solutions to deliver value quickly, from enhancing customer experiences to automating and accelerating internal software development processes.
Key Responsibilities
1. Research, Experimentation, and Integration of AI Solutions:
Proactively research and evaluate AI models, libraries and cloud-based AI services to identify the most suitable technologies for each use case.
Rapidly build Proof-of-Concept (PoC) prototypes to validate the feasibility and effectiveness of AI solutions before large-scale deployment.
Design system architectures and directly integrate AI solutions into the company's existing systems (Java backend, Flutter/Dart applications) via APIs. This includes writing and testing code to connect AI models or services to our applications, and supporting the development of a Model Context Protocol (MCP) Server to enable AI systems to safely interface with external tools and data sources
2. Process Automation and Optimization with AI:
Analyze the Software Development Lifecycle (SDLC) and propose AI-driven tools (e.g., AI Copilots, AIOps, AI for Testing) to improve efficiency and quality.
Contribute to internal solutions using retrieval-augmented generation (RAG) techniques to make the company's knowledge bases (Jira, Confluence, other documentation) more accessible and useful for employees.
Implement CI/CD pipelines for continuous integration and delivery of AI services, ensuring that new model versions and data updates can be rolled out efficiently. Monitor the performance, accuracy, and cost of AI systems in production (using logging, monitoring, and tracing frameworks) to detect issues or model drift early, and establish guardrails (governance policies, access controls, ethical AI guidelines) to ensure solutions are secure, compliant, and aligned with responsible AI practices.
Implement N8N workflow automation platform to resolve business requirement