AI and ML Engineer
- Strong programming skills in Python, and familiarity with Java or C++
- Solid understanding of machine learning algorithms and techniques
- Experience with deep learning frameworks (TensorFlow preferred)
- Familiarity with Google Cloud Platform (GCP) and its AI/ML services
- Knowledge of data processing and analysis techniques
- Strong analytical and problem-solving skills
- Excellent communication and collaboration abilities
- Implements basic machine learning models using scikit-learn and TensorFlow
- Uses Google Cloud AI Platform for model training and deployment
- Develops simple data pipelines using Google Cloud Dataflow
- Applies Google's pre-trained models (e.g., Vision API, Natural Language API) for basic tasks
- Understands and applies fundamental ML concepts (supervised/unsupervised learning, evaluation metrics)
- Creates basic visualizations of ML model performance
- Designs and implements custom neural network architectures using TensorFlow
- Utilizes Google Cloud AutoML for automated model development
- Develops more complex data processing pipelines using Apache Beam on Dataflow
- Implements advanced NLP solutions using BERT and other transformer models
- Applies Google Cloud AI Building Blocks for specialized tasks (e.g., Translation API, Speech-to-Text)
- Implements basic MLOps practices using Google Cloud AI Platform
- Architects end-to-end ML solutions using Google Cloud's AI platform
- Implements advanced deep learning models (CNNs, RNNs, GANs) for complex tasks
- Develops sophisticated NLP systems using Google's latest language models
- Utilizes Google Cloud Vertex AI for unified ML operations
- Implements ML pipelines using TensorFlow Extended (TFX)
- Applies Google's responsible AI practices in model development
- Designs scalable, production-grade AI systems using Google Cloud technologies
- Implements cutting-edge ML techniques (e.g., few-shot learning, meta-learning)
- Develops custom AI solutions using Google's open-source tools (e.g., JAX, Flax)
- Utilizes Google Cloud TPUs for high-performance model training
- Implements advanced MLOps practices, including ML pipelines and continuous training
- Leads initiatives for adopting Google's AI technologies across the organization
- Architects enterprise-wide AI strategies leveraging Google's AI ecosystem
- Implements sophisticated AI systems for multi-modal data (text, image, audio, video)
- Develops innovative solutions using Google's latest AI research (e.g., LaMDA, PaLM)
- Utilizes Google Cloud's AI solutions for edge and IoT applications
- Implements advanced techniques for model interpretability and fairness
- Drives adoption of Google's AI technologies for business transformation
- Shapes organizational AI/ML strategies aligned with Google's AI roadmap
- Pioneers adoption of cutting-edge Google AI technologies (e.g., quantum ML)
- Designs ultra-large-scale AI systems leveraging Google's distributed computing capabilities
- Creates frameworks for measuring and optimizing AI/ML impact on business outcomes
- Implements innovative approaches to AI governance and ethics using Google's guidelines
- Leads transformative initiatives in AI/ML practices and culture
- Google Cloud AI/ML Services: AI Platform, Vertex AI, AutoML, BigQuery ML
- TensorFlow Ecosystem: TensorFlow, Keras, TensorFlow Extended (TFX), TensorFlow.js
- Google AI APIs: Vision AI, Natural Language AI, Translation AI, Speech-to-Text, Text-to-Speech
- Google Cloud Data Tools: Dataflow, Dataprep, Data Fusion, Pub/Sub
- MLOps on Google Cloud: AI Platform Pipelines, Kubeflow
- Google's Responsible AI Practices and Tools
- Familiarity with Google's open-source AI projects (e.g., BERT, T5, JAX)
- Experience with Google Cloud TPUs and distributed training
- Knowledge of Google's AI research initiatives and latest publications
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