AI Application Engineer - LangGraph & Agentic AI
Belmont Lavan Ltd
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1,266 open application roles across 222 companies are on ApplySarthi right now, most of them in Bengaluru (101), Hyderabad (74), Delhi NCR (62).
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What application roles keep asking for: Python (13%) — counted across their open postings here.
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Belmont Lavan Ltd has 4 open roles listed here.
- Solution Architect - LangGraph & Agentic AI
- Solution Architect - LangGraph & Agentic AI
- AI Application Engineer - LangGraph & Agentic AI
Counted across 14 company job boards, updated as roles open and close.
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Practise the AI Application Engineer - LangGraph & Agentic AI at Belmont Lavan Ltd interview free →We are looking for an experienced AI Application Engineer to design and build intelligent applications powered by LLMs, LangGraph, and modern agentic AI technologies. You will focus on transforming business requirements into AI applications capable of reasoning through tasks, retrieving information, interacting with tools and enterprise systems, requesting human approval when required, and completing business processes. This role sits at the intersection of AI engineering, software development, workflow automation, and business process transformation . Requirements Agentic AI Application Development Design and develop AI applications using LangGraph and LLM technologies . Build agents capable of executing complex, multi-step business processes. Design stateful workflows incorporating reasoning, tool usage, validation, approvals, and exception handling. Develop single-agent and multi-agent solutions where appropriate. Translate business requirements into practical agentic AI architectures. LLM Application Engineering Integrate LLMs into production applications. Develop prompt strategies, structured outputs, tool calling, and context-management approaches. Select appropriate models based on accuracy, capability, latency, security, and cost. Develop mechanisms to improve reliability and reduce hallucinations. Implement appropriate guardrails around AI-generated decisions and actions. RAG and Enterprise Knowledge Design and implement Retrieval-Augmented Generation (RAG) solutions. Connect AI applications to enterprise documents, databases, APIs, and knowledge repositories. Develop retrieval and ranking strategies to provide agents with relevant context. Work with embeddings and vector databases. Implement data and context pipelines supporting AI agents. Business Process Automation Analyse business processes and identify opportunities for agentic automation. Design AI workflows that combine LLM reasoning with deterministic business logic. Build agents capable of retrieving information, making decisions, invoking tools, and completing actions. Implement human-in-the-loop approval and escalation processes. Ensure automated actions are controlled, auditable, and reversible where appropriate. Evaluation and Quality Develop evaluation frameworks for AI applications and agent workflows. Define metrics covering accuracy, task completion, reliability, latency, and cost. Build automated tests for prompts, agents, tools, and end-to-end workflows. Analyse failures and continuously improve agent behaviour. Use observability and evaluation data to optimise production systems. Production Deployment Deploy and operate AI applications in cloud and enterprise environments. Implement monitoring, logging, tracing, and performance management. Design resilient workflows with retries, timeouts, fallbacks, and recovery mechanisms. Work with DevOps and platform teams to establish appropriate deployment and CI/CD practices. Cross-Functional Collaboration Work closely with product managers, business analysts, software engineers, data scientists, architects, and business stakeholders. Communicate AI capabilities, limitations, risks, and implementation options. Help organisations identify realistic and valuable use cases for agentic AI. Required Experience Commercial experience developing AI/LLM applications . Hands-on experience with LangGraph and agentic workflow development. Strong Python development experience. Experience deploying AI applications into production. Strong understanding of LLMs, RAG, tool calling, structured outputs, and prompt engineering. Experience integrating AI applications with APIs, databases, enterprise systems, or SaaS platforms. Experience with cloud platforms such as AWS, Azure, or GCP . Experience with AI evaluation, monitoring, and observability. Desirable Skills LangChain / LangSmith Multi-agent systems AI workflow orchestration Vector databases Kubernetes Docker FastAPI Data pipelines MLOps AI security and governance Enterprise process automation Experience with financial services, healthcare, retail, manufacturing, or other complex enterprise environments Find more English Speaking Jobs in France on Arbeitnow
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Listed on arbeitnow · posted 2026-09-16. ApplySarthi collects openings and links to application pages; the role is advertised by Belmont Lavan Ltd, not by us.