Deep Learning Engineer

3 days ago

Bengaluru Chennai, India Tata Consultancy Services Full time ₹ 20,00,000 - ₹ 25,00,000 per year

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 Masters (preferably) in Computer Science, Data Science, Artificial Intelligence, or related fields.

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