We are looking for a highly skilled AI Agent Engineer to develop autonomous AI agents that interact, reason, and adapt to dynamic environments. You will design intelligent systems that utilize machine learning models, natural language processing (NLP), and reinforcement learning techniques to create agents capable of reasoning, learning, and decision-making in real-time.
Responsibilities:
Agent Design: Develop architectures for autonomous AI agents, utilizing techniques like reinforcement learning (RL), multi-agent systems (MAS), and decision-making frameworks (e.g., Markov Decision Processes).
NLP & Dialogue Systems: Build conversational AI agents using state-of-the-art NLP techniques (e.g., transformers, BERT, GPT-4, T5) and frameworks like spaCy or Rasa.
ML Model Integration: Train, fine-tune, and optimize deep learning models (using TensorFlow, PyTorch, or Keras) for various agent tasks like perception, planning, and execution.
Autonomous Learning: Implement systems for continuous learning and adaptation using reinforcement learning (RL), supervised, and unsupervised learning methods.
AI Ethics: Incorporate fairness, interpretability, and transparency into AI agents, ensuring compliance with ethical AI principles.
Agent Integration: Design and implement systems to integrate AI agents into existing products, ensuring performance and reliability under production loads.
Evaluation: Set up testing and evaluation pipelines for measuring agent performance (accuracy, task completion rate, response time) and improvement.
Technical Skills:
Machine Learning: Deep understanding of reinforcement learning, deep learning (CNNs, RNNs), NLP, and computer vision techniques.
NLP Frameworks: Expertise in transformer models (e.g., GPT-3/4, BERT, T5), and frameworks such as spaCy, Hugging Face Transformers, AllenNLP, and OpenAI API.
Frameworks: Proficient in deep learning frameworks like TensorFlow, PyTorch, Keras, and OpenAI Gym for reinforcement learning.
Algorithms: Familiarity with search algorithms, decision trees, and Markov models for autonomous decision-making.
Programming: Advanced proficiency in Python, C++, and potentially Java for high-performance applications.
Cloud Services: Familiarity with cloud-based AI services (AWS SageMaker, Google AI Platform) for scalable model training and deployment.
Nice to Have:
Experience with multi-agent reinforcement learning (MARL).
Knowledge of graph neural networks or neural-symbolic systems.
Experience working with edge AI and deployment on IoT devices.
