Ai engineer

9 hours ago

Agra, India NyxaLabs Full time

We're seeking an exceptional AI Engineer with deep expertise in Tensor Flow 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 Tensor Flow and Tensor Flow 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 Sage Maker 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 Tensor Flow 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 Tensor Flow 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 Sage Maker 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 Num Py, Pandas, and scikit-learn Knowledge of model compression techniques (quantization, pruning, distillation) Preferred Skills: Experience with Hugging Face Transformers, Lang Chain, or similar LLM frameworks Familiarity with Py Torch or JAX in addition to Tensor Flow Knowledge of reinforcement learning from human feedback (RLHF) techniques Experience with vector databases (Pinecone, Weaviate, Chroma DB) 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: Tensor Flow, Keras, Tensor Flow Serving, TFX Cloud: AWS Sage Maker, AWS Bedrock, EC2, S3, Lambda Languages: Python, SQL MLOps: MLflow, Weights & Biases, Kubeflow Tools: Jupyter, Git, Docker, Tensor Board

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