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
- Up-to-date 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
Required Experience:
Senior IC
📌 Senior AI Engineer (Bengaluru)
🏢 Sapiens
📍 Bengaluru
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