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Duties:
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Key Responsibilities:
- Design, train, and fine-tune large language models (e.g., GPT, LLaMA, PaLM) for various applications.
- Conduct research on cutting-edge techniques in natural language processing (NLP) and machine learning to improve model performance.
- Explore advancements in transformer architectures, multi-modal models, and emergent AI behaviors.
- Collect, clean, and preprocess large-scale text datasets from diverse sources.
- Develop and implement data augmentation techniques to improve training data quality.
- Ensure data is free from bias and aligned with ethical AI standards.
- Optimize model architecture to improve accuracy, efficiency, and scalability.
- Implement techniques to reduce latency, memory footprint, and inference time for real-time applications.
- Collaborate with MLOps teams to deploy LLMs into production environments using Docker, Kubernetes, and cloud
- Develop robust evaluation pipelines to measure model performance using key metrics like accuracy, perplexity, BLEU, and F1 score.
- Continuously test for bias, fairness, and robustness of language models across diverse datasets.
- Conduct A/B testing to evaluate model improvements in real-world applications.
- Stay updated with the latest advancements in generative AI, transformers, and NLP research.
- Contribute to research papers, patents, and open-source projects.
- Present findings and insights at conferences and internal knowledge-sharing sessions.
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Skills:
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Qualifications:
- Masters degree with a minimum of 3+ years experience post graduation or
- Bachelors degree with a minimum of 5+ years experience post graduation
- Advanced degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Strong programming skills.
- Proficiency with deep learning frameworks such as TensorFlow, PyTorch, or JAX.
- Hands-on experience with transformer-based models (e.g., GPT, BERT, RoBERTa, LLaMA).
- Expertise in natural language processing (NLP) and sequence-to-sequence models.
- Familiarity with Hugging Face libraries and OpenAI APIs.
- Experience with MLOps tools like Docker, Kubernetes, and CI/CD pipelines.
- Strong understanding of distributed computing and GPU acceleration using CUDA.
- Knowledge of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback).
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Education:
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- Masters degree with a minimum of 3+ years experience post graduation or
- Bachelors degree with a minimum of 5+ years experience post graduation
- Advanced degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
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Languages:
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Required
- EVALUATION PIPELINE DEVELOPMENT
- LARGE LANGUAGE MODEL FINE-TUNING
- NATURAL LANGUAGE PROCESSING
- MACHINE LEARNING
- LLM Application Development
Additional
- TENSORFLOW
- LATENCY REDUCTION
- MEMORY FOOTPRINT REDUCTION
- INFERENCE TIME REDUCTION
- MLOPS
- DOCKER
- KUBERNETES
- CLOUD DEPLOYMENT
- JAX
- TRANSFORMER-BASED MODELS
- ACCURACY MEASUREMENT
- BLEU SCORE MEASUREMENT
- F1 SCORE MEASUREMENT
- ROBUSTNESS TESTING
- LARGE LANGUAGE MODEL DESIGN
- LARGE LANGUAGE MODEL TRAINING
- TRANSFORMER ARCHITECTURES
- MULTI-MODAL MODELS
- EMERGENT AI BEHAVIORS
- TEXT DATASET COLLECTION
- TEXT DATASET CLEANING
- TEXT DATASET PREPROCESSING
- DATA AUGMENTATION
- ETHICAL AI STANDARDS
- MODEL ARCHITECTURE OPTIMIZATION
- A/B TESTING
- GENERATIVE AI ADVANCEMENTS
- RESEARCH PAPER CONTRIBUTION
- PATENT CONTRIBUTION
- OPEN-SOURCE PROJECT CONTRIBUTION
- CONFERENCE PRESENTATIONS
- INTERNAL KNOWLEDGE-SHARING SESSIONS
- DEEP LEARNING FRAMEWORKS
- PYTORCH
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Languages:
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English( Speak, Read, Write )
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Minimum Degree Required:
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Bachelor's Degree
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