Agentic AI Developer Thoughtscrest Software (opc

2.0 - 5.0 years

7 - 15 Lacs

bengaluru

Work from Office

We are seeking a passionate and skilled Agentic AI Developer with 2 to 5 years of experience in developing intelligent, autonomous systems using modern AI/ML technologies. In this role, you will design and deploy AI agents that demonstrate reasoning, adaptability, and goal-directed behaviorinteracting proactively with tools, APIs, and digital environments. Key Responsibilities Design, build, and deploy agentic AI systems capable of autonomous, goal-oriented decision-making. Develop AI agents using LLMs, reinforcement learning, memory modules, and multi-agent system architectures. Integrate AI models with external tools, APIs, databases, and software systems to facilitate real-world interactions. Collaborate with cross-functional teams including product managers and data scientists to convert business requirements into intelligent AI solutions. Implement memory and feedback mechanisms to create adaptive, context-aware agent behavior. Optimize performance, scalability, and reliability of AI systems in production environments. Required Skills & Qualifications 2 - 5 years of hands-on experience in AI/ML development and deployment. Proficiency in Python and relevant AI/ML libraries (e.g., LangChain, Hugging Face Transformers, TensorFlow, PyTorch, OpenAI APIs). Strong understanding of agentic AI concepts, including autonomous agents, memory, planning, and decision-making. Familiarity with LLM agent frameworks such as LangChain, AutoGPT, CrewAI, or OpenAgents. Experience with RESTful APIs, microservices architecture, and system integrations. Excellent problem-solving and analytical skills with the ability to think independently. Education Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline. Preferred (Good to Have) Experience in fintech or enterprise-grade AI solutions. Familiarity with cloud platforms such as AWS, GCP, or Azure. Knowledge of agent simulation environments or multi-agent reinforcement learning (MARL).

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