AI Engineer

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