AI Engineer

18 hours ago

Rajkot, India NyxaLabs Full time

We're seeking an exceptional AI Engineer with deep expertise in TensorFlow model training to design and build next-generation AI systems. This role focuses on developing sophisticated machine learning models, particularly Large Language Models and NLP solutions, while leveraging AWS cloud infrastructure for scalable deployment. Key Responsibilities: - Design and architect enterprise-scale AI/ML solutions with emphasis on custom model development and training - Build, train, and optimize deep learning models using TensorFlow and TensorFlow Extended (TFX) - Develop and fine-tune Large Language Models for domain-specific applications - Implement advanced NLP pipelines including text classification, named entity recognition, sentiment analysis, and language generation - Lead model training infrastructure design, including distributed training strategies and GPU optimization - Deploy and manage ML models on AWS SageMaker and AWS Bedrock platforms - Establish MLOps practices for model versioning, experiment tracking, and continuous training - Optimize model architectures for performance, accuracy, and computational efficiency - Conduct thorough model evaluation, validation, and performance benchmarking - Collaborate with data engineering teams to build robust training data pipelines - Mentor ML engineers and data scientists on TensorFlow best practices and model training techniques Required Qualifications: - 3+ years of hands-on experience in machine learning engineering and AI architecture - Expert-level proficiency in TensorFlow 2.x for model development and training - Deep understanding of neural network architectures (Transformers, CNNs, RNNs, attention mechanisms) - Proven track record training large-scale models, including experience with LLMs - Strong expertise in Natural Language Processing and modern NLP techniques - Extensive experience with AWS cloud services, particularly SageMaker and Bedrock - Solid understanding of training optimization techniques (learning rate scheduling, regularization, gradient accumulation) - Experience with distributed training frameworks and multi-GPU/TPU training - Strong Python programming skills and experience with NumPy, Pandas, and scikit-learn - Knowledge of model compression techniques (quantization, pruning, distillation) Preferred Skills: - Experience with Hugging Face Transformers, LangChain, or similar LLM frameworks - Familiarity with PyTorch or JAX in addition to TensorFlow - Knowledge of reinforcement learning from human feedback (RLHF) techniques - Experience with vector databases (Pinecone, Weaviate, ChromaDB) for RAG applications - Understanding of prompt engineering and few-shot learning strategies - Experience with Kubernetes and containerization (Docker) for ML workloads - Publications or contributions to open-source ML projects Technical Skills: - Frameworks: TensorFlow, Keras, TensorFlow Serving, TFX - Cloud: AWS SageMaker, AWS Bedrock, EC2, S3, Lambda - Languages: Python, SQL - MLOps: MLflow, Weights & Biases, Kubeflow - Tools: Jupyter, Git, Docker, TensorBoard

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