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.
ResponsibilitiesKey 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 metrics
- Latency, reliability, and system 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
Understands operational impact of AI systems on business processes
Able to balance performance, cost, and quality trade-offs
Communicates effectively with technical and business stakeholders
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
Required Education
- Master’s degree (Ph. D. is a plus) in Science, Technology, Engineering, Computer Science,
Bachelor’s degree plus ASA or similar work experience is accepted in place of a relevant Master’s degree
Certifications on Cloud or Microservices or Kubernetes (CKAD) are plus.
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.
Skills Required
- 5-8 years of experience in software or machine learning engineering
- Hands-on experience with Python, APIs, and microservices architecture
- Experience with cloud environments, preferably Azure; AWS or GCP acceptable
- Experience with Kubernetes and containerized deployments
- Experience with generative AI and machine learning systems
- Experience with MLOps or LLMOps tooling
- Experience with monitoring and observability tools, including logs, metrics, and tracing
- Experience with data pipelines and distributed systems
- Understanding of GenAI and LLM systems, including RAG, embeddings, and prompting
- Understanding of machine learning lifecycle and deployment patterns
- Master's degree in science, technology, engineering, or computer science
- Bachelor's degree plus ASA or similar work experience may substitute for a relevant master's degree
- Cloud, microservices, or Kubernetes certifications such as CKAD
- Ph.D. in a relevant field
What We Do
SCOR, one of the world’s largest reinsurers, serves more than 5,000 clients worldwide, providing a diversified and innovative range of solutions to control and manage risk. SCOR delivers advanced financial solutions, analytics and services across all dimensions of risk in Life & Health, Property & Casualty, and Investments. Reinsurance lies at the intersection of technical expertise and scientific progress. Models, data, and pricing and reserving tools are essential, yet they are never sufficient on their own. Sound risk decisions require expert judgment, experience and perspective. This is what we call the Art and Science of Risk. Reinsurance is a knowledge industry, where expertise grows through accumulation, transmission and practice. Across the Group, 3,600 experts based in more than 35 offices worldwide contribute to this collective intelligence. Actuaries, underwriters, risk management specialists, and Tech & Data experts transform data into insight, explore extreme scenarios, define the boundaries of insurability and help anticipate emerging risks. Together, they strengthen the resilience of SCOR, our clients and the societies we serve. This expertise is built through shared experience,continuous questioning and collective reflection. Like artists, we belong to schools of thought, learning first to observe, then to replicate, and ultimately to innovate. This ongoing transmission of knowledge enables SCOR to develop a distinctive approach, combining rigor, creativity and long-term vision in the service of risk mastery. This shared commitment underpins SCOR’s role as a global reinsurer. By turning risk into resilience and sustainable value, our collective of experts acts with responsibility and purpose. Together, we help protect the future, and shape it, for our clients, for society and for generations to come.
