Sapiens

SaaS insurance software for core processes

Senior Agentic AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeIndiaNo salary listed

About the Job

Requirements

Demonstrated depth building and shipping production agentic AI systems; shipped systems are weighted over years in a title.
Strong hands-on experience with LangGraph or equivalent agentic orchestration frameworks, including custom orchestration.
Deep proficiency in Python, including writing clean, testable, production-ready code.
Experience designing and optimizing end-to-end retrieval-augmented generation systems, including indexing, retrieval, reranking, grounding, and evaluation.
Daily working proficiency with Claude (Anthropic API) and Claude Code.
Experience building and deploying agents on Azure AI Foundry or an equivalent enterprise cloud AI platform.
Practical understanding of large language model behavior, including strengths, limitations, hallucination risks, reasoning constraints, and evaluation methods.
Experience evaluating and debugging agent behavior at trajectory and task level, not only output quality.
Hands-on experience with Model Context Protocol-based interoperability patterns and tool-calling agent design.
Experience with modern software practices, including testing, continuous integration and continuous delivery, observability, tracing, and debugging for production large-language-model systems.

Responsibilities

Design and implement agentic systems for multi-step reasoning, planning, tool use, and workflow execution across 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 long-horizon reliability, including multi-step task completion, recovery from compounding errors, planning under uncertainty, and robust tool use when individual steps fail.
Build reasoning for high-stakes implementation decisions using criteria-grounded outputs, structured review patterns, and auditable rationales that delivery consultants can act on and defend.
Develop end-to-end retrieval-augmented generation pipelines covering ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.
Engineer memory and context management, including conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context selection.
Apply Model Context Protocol-style tool and context interfaces so agents access relevant information 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 behavior.
Apply guardrails, safety controls, and failure handling to reduce hallucinations in agents whose outputs practitioners use in live client settings.
Evaluate agents at trajectory and task level using multi-step task success, failure-mode and regression analysis, and 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 delivery workflows.
Deliver production-quality Python code using testing, continuous integration and continuous delivery, logging, versioning, and documentation; make architecture decisions balancing quality, reliability, latency, cost, and model risk.
Translate ambiguous, high-complexity implementation processes into robust system logic and reusable AI patterns, and translate advances in agentic systems and research into practical engineering decisions.

Desired Qualifications

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

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

About the Company

Sapiens

SaaS insurance software for core processes

Sapiens provides cloud-based software for the insurance industry, offering modules for policy administration, claims management, and underwriting to help insurers run operations more efficiently. Its products are accessed as a software-as-a-service, delivering end-to-end insurance workflows through integrated modules that handle policies, claims, and risk with regular updates. The company differentiates itself through a long track record since 1982, a broad, integrated product portfolio, and a global footprint, strengthened by acquisitions like Candela and AdvantageGo to expand capabilities and regional reach, especially in Asia-Pacific. Its goal is to help insurers deliver better customer experiences by providing scalable, cloud-based software and services to support core processes.

Company size: 5,001-10,000
Company stage: IPO
Headquarters: Holon, Israel
Founded: 1982

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