AI Architect - TensorFlow Model Training Specialist

About the Role

We're seeking an exceptional AI Architect 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.

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

Requirements

• 5+ 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)

Nice to Have

• 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
• AWS Machine Learning Specialty or Solutions Architect certification
• Experience with Kubernetes and containerization (Docker) for ML workloads
• Publications or contributions to open-source ML projects
• Master's or PhD in Computer Science, Machine Learning, or related field

Benefits

• Competitive salary and equity package
• Remote-first work environment
• Professional development budget
• Latest hardware and tools
• Health and wellness benefits