Join our team and play a pivotal role in developing advanced AI agents.
From data automation to AI applications, you will be responsible for designing, implementing, and optimizing AI agents / MCP that leverage LLMs for natural language understanding, autonomous decision-making, multi-step task execution and tool design. This role involves close collaboration with product teams, data engineers, data analysts, and software engineers to build scalable, intelligent systems that interact seamlessly with users and internal / external tools.
Design, develop, and fine-tune AI agents / MCP tools for complex multi-turn conversations and autonomous workflows. Integrate AI agents with external APIs, databases, and knowledge bases to enhance contextual understanding and functionality. Build and maintain pipelines for training, evaluating, and deploying AI agents-related in production. Collaborate with cross-functional teams to translate business requirements into AI-driven solutions. Monitor, debug, and optimize AI agent performance, ensuring reliability, scalability, and safety. Stay updated on the latest research and advances in LLMs, AI agent architectures, and related technologies.
Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Machine Learning, or related fields. Proficiency in Python and experience with AI/ML frameworks such as PyTorch or TensorFlow. Hands-on experience with LLMs and related toolkits. Familiarity with AI agent design, prompt engineering, and multi-agent systems. Experience with cloud platforms (AWS or Azure) and container orchestration technologies like Docker and Kubernetes. Strong analytical and problem-solving skills, with ability to work effectively in a team environment. Basic understanding of database management and data handling. Ability to communicate effectively in English, both verbally and in writing.
Research or project experience in LLMs or Agentic AI. Experience with multi-modal AI models, reinforcement learning, or knowledge graphs integration. Familiarity with MLOps, CI/CD pipelines, model versioning, and deployment workflows.
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