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

• 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

• 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

• 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

• 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

• 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