Deep Learning Engineer

Deep Learning and NLP Skills: Proficiency in building and training deep neural networks using frameworks like TensorFlow, PyTorch, etc. Understanding of transformer architectures like BERT, GPT, T5, etc. Knowledge of sequence-to-sequence models, attention mechanisms, and embeddings. Experience with NLP tasks such as tokenization, named entity recognition, and sentiment analysis. Reinforcement Learning: Strong understanding of the fundamentals of reinforcement learning, including concepts like Markov Decision Processes, Q-learning, and policy gradients. Experience with modern RL libraries like OpenAI Gym, Ray's RLlib, or Stable Baselines. Practical knowledge of reinforcement learning in non-stationary environments and using human feedback as rewards. Optimization Techniques: Familiarity with techniques like gradient clipping, learning rate schedules, regularization, and dropout. Understanding of distributed training and optimization techniques to handle large datasets and models. Computational Skills: Proficiency in Python and other programming languages common in the AI/ML domain. Knowledge of GPU and TPU computation, especially in the context of deep learning. Research Acumen: Ability to read, understand, and implement findings from scientific papers, especially those related to LLMs and their optimizations. Contributions to the AI research community through publications, conferences, or open-source projects can be an advantage. Deep Understanding of LLMs: Familiarity with the nuances of large language models, their strengths, and potential pitfalls. Awareness of typical business use cases for LLMs, from customer support automation to content generation. Low-code/No-code Platform Expertise: Proficiency in using and configuring platforms like Langflow. Experience in setting up, deploying, and managing applications on such platforms. Agent-based Modelling Skills: Strong understanding of defining, designing, and implementing agents, their behaviours, and interactions. Ability to set up policies, constraints, and orchestrate chains of agents to fulfil specific business processes or workflows. Educational Background: A Bachelor's or Master’s (preferably) in Computer Science, Data Science, Artificial Intelligence, or related fields.

Location: chennai, IN

Posted Date: 4/30/2025