Senior Agentic AI Engnieer

Senior Agentic AI Engineer

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.

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

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