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manipulation and autonomous task execution in collaboration with robotics engineers.\nIntegrate robots with Digital Twin platforms, IoT systems and Smart Command Centres.\nRelevant Technical Skills\nGenerative AI: LLMs, Prompt Engineering, Fine-tuning, Agentic RAG and Advanced RAG Pipelines.\nAI Agents: Multi-Agent Systems, LangGraph, AutoGen and AI workflow orchestration.\nAI Security: LLM Guardrails, Responsible AI, Governance, Evaluation and Compliance.\nMultimodal AI: Text, Voice, Vision, Conversational AI and Vision-Language Models.\nRobotics AI: ROS/ROS2, Robot SDKs, Vision-Language-Action (VLA) Models, Robot Perception and Physical AI.\nAI Operations: LLMOps, AI Monitoring, Observability, Model Evaluation and Deployment.\nCloud Platforms: AWS, Microsoft Azure or Google Cloud Platform.\nProgramming & Integration: Python, FastAPI, REST APIs, Vector Databases and LLM Frameworks.\nCandidate Requirements\nMinimum 2 years of relevant experience in GenAI, LLM applications, AI automation or related AI engineering.\nStrong Python programming and API integration skills.\nExperience developing and deploying AI agents or production-grade AI applications.\nGood understanding of AI model evaluation, performance, security and scalability.\nExperience in digital marketing automation or humanoid robotics will be an added advantage.\nAbility to work independently and collaborate with multidisciplinary engineering teams.\nSkills\nROS, Multi-Agent Systems, Retrieval-Augmented Generation (RAG), Distributed Platforms, Human-Robot Collaboration, AI Agents, Multimodal AI, Model Deployment, Governance, Robot, Action language, Conversational AI, Workflow Orchestration, Robot Framework, Database Management Systems, Vision Language Models 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