Senior AI Engineer

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.

Work Youll 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