Job Description
Start your career where innovation meets real impact.
Sapiens Accelerate is our global Early Careers Programme, designed for graduates with up to 18 months of experience, who are ready to learn, contribute, and shape the future of insurance. From day one, you'll work on meaningful projects, collaborate with experts across the business, build practical AI skills, and develop the capabilities needed to thrive in a global technology company.
Role purpose:
The L2 Support Engineer handles escalated, complex, and technically demanding issues that go beyond L0 scope, including data-related, AI coding, configuration, integration, and -behaviour problems specific to our AIP product. As L3/engineering is not part of this organisation, the L2 role also acts as the technical bridge to the FDE’s. Additionally working with, product/engineering/cloud team when a defect or product limitation is confirmed and owns the resolution process end-to-end from a support perspective.
Responsibilities
- Own tickets conducting deep-dive technical investigation and root-cause analysis
- Software Design & Architecture: Understanding how large-scale enterprise systems connect via APIs.
- Data Structures & Algorithms: Maintaining clean, optimized performance under high-pressure real-world environments.
- Cloud Computing & DevOps: Hands-on experience with AWS, Azure, GCP, or containerization tools like Docker and Kubernetes.
- Applied AI / Machine Learning: Understanding Generative AI workflows, Retrieval-Augmented Generation (RAG), and vector databases—which are heavily used in modern FDE roles
- Trouble Shooting: Reproduce complex issues in test/staging environments
- Distinguish between user error, configuration issues, code errors, and genuine product defects
- Manage the interface with Engineering/Product for confirmed bugs: prepare technical write-ups, reproduction steps, business impact assessments; track fixes through release and validate with the customer
- Support and adjust L0 Agents: review escalations, provide troubleshooting guidance, contribute to L0 AI agent training
- Own and expand the technical knowledge base (advanced troubleshooting guides, known-issue articles, workarounds)
- Perform basic data corrections/queries where appropriate and within governance guidelines (e.g., SQL scripts under change control)
- Support integration-related troubleshooting (APIs, file feeds, third-party connections common in insurance ecosystems: policy admin, claims, payment providers)
- Participate in post-incident reviews and contribute to problem management
- Ensure all actions and data changes are properly documented and auditable, in line with insurance industry compliance requirements
Reporting to: Customer Support Team Lead / Manager
Requirements
Experience
- 0–18 months technical support, application support, or a related engineering-adjacent role
Skills & Competencies:
- Strong analytical and root-cause troubleshooting skills
- Demonstrated knowledge to enable troubleshooting Python, TypeScript, Java, and modern AI/LLM frameworks (e.g., LangChain).
- Knowledge of Cloud platforms (AWS, Azure), Kubernetes, containerization, SQL/vector databases, and REST APIs.
- Experience with ticketing and monitoring tools (Zendesk/Jira, log aggregators such as ELK/Splunk/Datadog, APM tools)
- Basic understanding of REST APIs, integrations, and system architecture
- Ability to explain technical issues clearly to both technical and non-technical audiences
- Strong ownership mentality — able to drive an issue to resolution without L3 backup
- Ability to support development of L0 AI agents with creation of knowledge and other artifacts
- Comfortable working with sensitive/regulated data and following change control processes
- Working with AI Agents as part of support team
Education
- Bachelor of Science (BSc) or Master of Science (MSc)
- Computer Engineering, Computer Science, Data Science / Analytics, Software Engineering, AI
- Other highly quantitative or STEM fields (e.g., Mathematics, Physics, or Management Information Systems),
Language:
Success metrics (KPIs):
- Escalated ticket resolution time within SLA
- Root-cause identification rate (vs. workaround-only resolutions)
- Quality and speed of engineering handover documentation for confirmed defects
- Reduction in repeat/recurring escalations
- CSAT on escalated/complex cases
- L0 knowledge base contribution impact
- AI Usage
