Arting Digital Private Limited
GenAI / Agentic AI Engineer
Jalalabad, INIn sedeIndeterminatoTempo pieno
Pubblicato il 26 set 2026
Questa offerta è pubblicata in EN
Position : GenAI / Agentic AI Engineer
Experience: 8+ Years Location: Gurgaon / Mumbai / Bangalore
Role Overview
Key Responsibilities
Must-Have Skills
Experience: 8+ Years Location: Gurgaon / Mumbai / Bangalore
Role Overview
We are looking for a Senior GenAI / Agentic AI Engineer with strong hands-on experience building and deploying production-grade GenAI and Agentic AI solutions.
Key Responsibilities
- Design and develop LLM-powered applications using OpenAI, Anthropic, Azure OpenAI.
- Build multi-agent systems using LangGraph, LangChain, AutoGen, CrewAI, or similar frameworks.
- Develop RAG pipelines, AI agents, tool calling, memory, planning, guardrails, and HITL workflows.
- Build scalable APIs and services using Python, FastAPI, and async programming.
- Work with vector databases such as FAISS, Pinecone, and pgvector.
- Implement prompt engineering, LLM evaluation, monitoring, and observability.
- Deploy solutions on Azure/AWS/GCP, with understanding of secure credential management, RBAC, Managed Identity, and enterprise integrations.
- Integrate with platforms such as Microsoft Graph API, Power Automate, and Salesforce.
- Make sound architectural decisions between deterministic workflows and LLM-based judgment.
Must-Have Skills
- 8+ years software engineering experience with strong GenAI/Agentic AI delivery experience.
- Proven production deployment experience—not just POCs.
- Strong Python + FastAPI skills.
- Hands-on with LLMs, RAG, vector DBs, and agentic frameworks.
- Strong understanding of multi-agent architecture, HITL, guardrails, and observability.
- Experience with Azure or equivalent AWS/GCP cloud services.
Good to Have: n8n/workflow automation, Docker/Kubernetes, CI/CD, LLM cost & performance optimization.
Informazioni ruolo
Tipo di lavoro
Tempo pieno
Skill richieste
GenAI / Agentic AI EngineeringProduction Deployment of GenAI SolutionsLLM Platforms (OpenAI, Anthropic, Azure OpenAI)Multi-agent Frameworks (LangGraph, LangChain, AutoGen, CrewAI)Retrieval-Augmented Generation (RAG) pipeline developmentVector Databases (FAISS, Pinecone, pgvector)Prompt Engineering and LLM EvaluationTool Calling, Memory, Planning, Guardrails and HITL WorkflowsPython, FastAPI and Async ProgrammingCloud Deployment and Security (Azure/AWS/GCP, credential management, RBAC, Managed Identity)Enterprise Integrations (Microsoft Graph API, Power Automate, Salesforce)Architectural Decision-Making (deterministic workflows vs LLM-based judgment)Containerization and CI/CD (Docker, Kubernetes, CI/CD)Workflow Automation (n8n)LLM Cost and Performance Optimization
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