Senior Agentic AI Engineer

Role Title: Senior Agentic AI Engineer

About Us

Sapiens International Corporation N.V. is a global leader in intelligent, SaaS-based software solutions. With Sapiens’ robust platform, customer-driven partnerships, and rich ecosystem, insurers are empowered to future-proof their organizations with operational excellence in a rapidly changing marketplace.

Our solutions help insurers harness the power of AI and advanced automation to support core solutions for property and casualty, workers’ compensation, and life insurance, including reinsurance, financial & compliance, data & analytics, digital, and decision management.

Sapiens boasts a longtime global presence, serving over 600 customers in more than 30 countries with our innovative offerings. Recognized by industry experts and selected for the Microsoft Top 100 Partner program, Sapiens is committed to partnering with our customers for their entire transformation journey and is continuously innovating to ensure their success.

About The Role

Insurance software implementations are among the most complex, document-heavy, and process-intensive programmes in enterprise technology. A single implementation can involve thousands of configuration decisions, hundreds of requirement documents, and years of delivery time. Sapiens is rebuilding how that work gets done — using production-grade AI agents that operate across the full implementation lifecycle, from pre-sales and scoping through to configuration, testing, and go-live.

Work You'll Do

Agent architecture & orchestration

Design and implement agentic systems capable of multi-step reasoning, planning, tool use, and workflow execution against complex, document-intensive implementation processes Build stateful workflows using LangGraph or equivalent — including branching, retries, self-correction, human-in-the-loop checkpoints, and reusable orchestration patterns Engineer for long-horizon reliability — multi-step task completion, recovery from compounding errors, planning under uncertainty, and robust tool use when individual steps fail Build the reasoning behind high-stakes implementation decisions — criteria-grounded outputs, structured review patterns, and auditable rationales that delivery consultants can act on and defend

Retrieval, grounding & context engineering

Develop end-to-end RAG pipelines: ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies Engineer memory and context management — conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context selection Apply MCP-style tool and context interfaces so agents access the right information at the right time across enterprise knowledge repositories, document sources, and structured configuration data

Reliability, evaluation & safety

Implement observability and tracing for prompts, tool calls, retrieval quality, agent traces, failures, drift, latency, and production behaviour Apply guardrails, safety controls, and failure-handling to reduce hallucinations in agents whose outputs practitioners act on directly in live client settings Evaluate agents at trajectory and task level — multi-step task success, failure-mode and regression analysis, sandboxed test environments — alongside retrieval and generation quality metrics, automated checks, and human review

Integration & production craft

Build integrations with internal and external tools, APIs, enterprise systems, databases, and model providers so agents operate reliably within real delivery workflows Deliver production-quality Python code with strong practices in testing, CI/CD, logging, versioning, and documentation; make architecture decisions that balance quality, reliability, latency, cost, and model risk Translate ambiguous, high-complexity implementation processes into robust system logic and reusable AI patterns; stay current with advances in agentic systems and translate research into practical engineering decisions

Required Qualifications

Demonstrated depth building and shipping production agentic AI systems — we weigh shipped systems over years in a title Strong, hands-on experience with LangGraph or equivalent agentic orchestration frameworks, including custom orchestration Deep proficiency in Python — clean, testable, production-ready code Experience designing and optimising end-to-end RAG systems: indexing, retrieval, reranking, grounding, and evaluation Daily working proficiency with Claude (Anthropic API) and Claude Code — you use these tools every day, not occasionally Experience building and deploying agents on Azure AI Foundry or an equivalent enterprise cloud AI platform Practical understanding of LLM behaviour — strengths, limitations, hallucination risks, reasoning constraints, and the evaluation methods used to measure them Experience evaluating and debugging agent behaviour at trajectory and task level, not just output quality Hands-on experience with MCP-based interoperability patterns and tool-calling agent design Modern software practices: testing, CI/CD, observability, tracing, and debugging for LLM-based systems in production

Preferred Qualifications

Experience with multi-agent orchestration and agent collaboration patterns Familiarity with vector databases — Pinecone, Weaviate, Azure AI Search, OpenSearch Experience building agents that process complex, unstructured document types — contracts, RFPs, configuration files, regulatory documents Exposure to model adaptation techniques such as LoRA or QLoRA Prior work in insurance, financial services, or enterprise SaaS implementation environments Demonstrated habit of staying current with AI research, benchmarks, and emerging engineering patterns .

Sapiens is an equal opportunity employer. We value diversity and strive to create an inclusive work environment that embraces individuals from diverse backgrounds.

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