Modern AI Solution Developer

1 day ago

Thoothukudi, Tamil Nadu, India beBeeArtificialintelligenceengineer Full time US$ 1,50,000 - US$ 1,70,000

Job Description

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We are seeking an experienced AI Engineer to design, develop, and deploy modern AI solutions. In this role, you'll build context-aware AI agents capable of dynamic memory management, tool-based reasoning, multi-modal interaction, and consistent state maintenance across distributed AI ecosystems.

Key Accountabilities:

  • Design, develop, and deploy advanced AI systems leveraging Large Language Models (LLMs), multi-modal models, and autonomous agent frameworks.
  • Architect and implement Retrieval-Augmented Generation (RAG) pipelines integrated with vector databases for dynamic, enterprise-safe context retrieval.
  • Develop and manage context-aware AI agents leveraging Model Context Protocol (MCP) or equivalent structured context management mechanisms for persistent memory, multi-agent collaboration, and session continuity.
  • Implement context propagation, state serialization, and contextual handoff mechanisms across distributed agents, tools, and reasoning chains.
  • Integrate AI systems with cloud APIs, vector databases, plugin architectures, and orchestration frameworks for multi-step task execution.
  • Optimize memory management, prompt engineering, and dynamic context windows for complex, long-running AI workflows.
  • Lead AI agent orchestration initiatives using frameworks like LangChain, Semantic Kernel, CrewAI, or AutoGen, with MCP integration for context tracing and governance.

Requirements:

  • 3+ years of AI engineering experience, with direct expertise in Generative AI, Agentic AI orchestration frameworks, Retrieval-Augmented Generation, and Model Context Protocol.
  • Strong software engineering skills in Python, including AI/ML libraries like PyTorch, TensorFlow, Transformers, and RAG toolkits.
  • Hands-on experience working with vector databases and plugin-based AI architectures.
  • Programming framework/language: Python, JS or Java.

Preferred Skills:

  • Experience integrating multi-modal models into agentic workflows.
  • Familiarity with AI safety, observability, and context integrity management in distributed AI systems.
  • Exposure to LLMOps pipelines, prompt optimization techniques, and AI agent performance tuning.

Education Qualification:

  • Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.

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