AI Operations Lead

Job Summary

The AI Operations Lead contributes hands-on to integration, deployment and monitoring of AI systems such as ML, GenAI, RAG and agentic workflows. The role focuses on a subset of products/services and ensures operational excellence, observability, and performance. 

Responsibilities

Key duties and responsibilities

• Implement and operate integration of AI capabilities into enterprise products following standard patterns 

• Contribute to deployment of: 

  • RAG pipelines

  • Copilots and AI assistants

  • Agentic workflows

  • Predictive ML services

•  Support delivery squads in integrating AI services into business applications 

• roubleshoot and resolve integration or runtime issues in production

AI Observability & Monitoring (Core focus)

• Design and implement AI observability frameworks, including: 
  • Model performance monitoring (drift, quality, hallucination signals)
  • Usage and adoption of metrics
  • Latency, reliability, and health
• Ensure proper logging, tracing, and monitoring of AI pipelines
• Contribute to definition of AI SLAs/SLOs aligned with business expectations

• Support incident management and post-mortem analysis for AI systems

   

Cost & Performance Optimization

• Monitor AI-related cloud consumption and inference costs
• Optimize pipelines for efficiency (model selection, caching, orchestration)
• Contribute to FinOps practices specific to AI workloads

 

Business Acumen

• Understand operational impact of AI systems on business processes

• Able to balance performance, cost, and quality trade-offs

• Communicates effectively with technical and business stakeholders

 

Qualifications

Required experience & competencies

• 5–8 years in software/ML engineering 

• Cloud (Azure), Kubernetes, Python 

• Experience with GenAI and ML systems 

Technical Skills

• Strong hands-on experience in: 
  • Python, APIs, microservices architecture
  • Cloud environments (Azure preferred, AWS/GCP acceptable)
  • Kubernetes and containerized deployments
• Experience with: 
  • MLOps / LLMOps tooling
  • Monitoring/observability tools (e.g., logs, metrics, tracing)
  • Data pipelines and distributed systems
• Understanding of: 
  • GenAI / LLM systems (RAG, embeddings, prompting)
  • ML lifecycle and deployment patterns

Soft skills

• Hands-on and problem-solving mindset
• Ability to debug complex AI systems in production 
• Strong collaboration with engineering and product teams 
• Ability to explain technical issues clearly to non-experts 
•  Proactive and continuous improvement mindset

Business acumen

• Can adapt his/her speech to make relevant for business users
• Can interact effectively with top management
• Can support in produce presentations or architecture material

As a leading global reinsurer, SCOR offers its clients a diversified and innovative range of reinsurance and insurance solutions and services to control and manage risk. Applying “The Art & Science of Risk,” SCOR uses its industry-recognized expertise and cutting-edge financial solutions to serve its clients and contribute to the welfare and resilience of society in around 160 countries worldwide.

Working at SCOR means engaging with some of the best minds in the industry – actuaries, data scientists, underwriters, risk modelers, engineers, and many others – as we work together to find solutions to pressing challenges facing societies.

As an international company, our common culture is defined by “The SCOR Way.” Serving both to build momentum that drives the Group forward and as a compass to guide our actions and choices, The SCOR Way is anchored by five core values, reflecting the input of employees at all levels of the Group. We care about clients, people, and societies. We perform with integrity. We act with courage. We encourage open minds. And we thrive through collaboration.

SCOR supports inclusion and the diversity of talents, and all positions are open to people with disabilities.