Senior Artificial Intelligence Engineer

Sapiens; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Sapiens is building production-grade AI agents to transform insurance software implementations, spanning pre-sales to go-live. You will architect agentic systems, design memory and context management, and deploy robust RAG pipelines across enterprise data sources.

As a Senior AI Engineer, you will craft reliable long-horizon AI solutions, implement safety controls, and collaborate with teams to deliver scalable, repository-driven production code in a cloud-first environment.

Qualifications

• Shipped production agentic AI systems with multi-step reasoning and tool use.
• Strong hands-on experience with LangGraph or equivalent orchestration frameworks.
• Deep understanding of Python, testable production-ready code.
• Experienced in end-to-end RAG systems: indexing, retrieval, grounding, evaluation.
• Daily use of Claude and Claude Code in production settings.

Responsibilities

• Design and implement agentic architectures for multi-step reasoning and planning.
• Build stateful workflows with branching, retries, and human-in-the-loop checks.
• Ensure reliability through robust tool usage and memory management.
• Develop end-to-end retrieval, grounding, and context engineering pipelines.
• Implement observability, tracing, and safety controls for live systems.
• Integrate with enterprise tools, APIs, and data sources in production workflows.

Skills

Python

LLM Expertise

Multi-agent orchestration

Production-grade software

Tools

LangGraph

Claude (Anthropic API)

Azure AI Foundry

Pinecone

Weaviate

OpenSearch

Job description

Senior AI Engineer Senior Agentic AI Engineer

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

Experience Level

Senior Level