Senior Agentic Ai Engineer Sapiens Bengaluru

Role

Senior Agentic AI Engineer responsible for designing and implementing production-grade agentic systems to automate complex, document-intensive insurance implementation processes. The role covers agent architecture and orchestration, retrieval and grounding, reliability and safety, evaluation, and integrations with enterprise tools and model providers.

Key Responsibilities

• Design and implement agentic systems capable of multi-step reasoning, planning, tool use, and workflow execution across implementation lifecycles.
• Build stateful workflows (branching, retries, self-correction, human-in-the-loop checkpoints) using LangGraph or equivalent orchestration frameworks.
• Engineer for long-horizon reliability: multi-step task completion, recovery from compounding errors, planning under uncertainty.
• Develop end-to-end RAG pipelines: ingestion, chunking, embeddings, vector/hybrid retrieval, reranking, contextual compression, and grounding strategies.
• Implement conversational state and persistent memory, retrieval-aware context assembly, and token-efficient context selection.
• Apply MCP-style tool and context interfaces for agent access to enterprise knowledge repositories 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 used in live client settings.
• Evaluate agents at trajectory and task level with sandboxed tests, regression analysis, automated checks, and human review.
• Build integrations with internal/external tools, APIs, enterprise systems, databases, and model providers.
• Deliver production-quality Python code with strong testing, CI/CD, logging, versioning, and documentation practices.
• Translate ambiguous implementation processes into robust system logic and reusable AI patterns.

Requirements

• Demonstrated experience building and shipping production agentic AI systems.
• Strong, hands-on experience with LangGraph or equivalent agentic orchestration frameworks, including custom orchestration.
• Deep proficiency in Python with production-grade code, testing, and documentation practices.
• Experience designing and optimizing end-to-end RAG systems: 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 equivalent enterprise cloud AI platforms.
• Practical understanding of LLM behavior, hallucination risks, reasoning constraints, and evaluation methods.
• Experience evaluating and debugging agent behavior at trajectory and task level.
• Hands-on experience with MCP-based interoperability patterns and tool-calling agent design.
• Modern software engineering practices for LLM systems: testing, CI/CD, observability, tracing, and debugging.

Preferred Qualifications

• Experience with multi-agent orchestration and agent collaboration patterns.
• Familiarity with vector databases such as Pinecone, Weaviate, Azure AI Search, OpenSearch.
• Experience processing complex, unstructured document types (contracts, RFPs, configuration files, regulatory documents).
• Exposure to model adaptation techniques like LoRA or QLoRA.
• Prior experience in insurance, financial services, or enterprise SaaS implementation environments.
• Habit of staying current with AI research, benchmarks, and emerging engineering patterns.