Generative Ai Engineer
3 weeks ago
Responsibilities:
Design, develop, and deploy production-ready AI and ML models using Google Cloud Platform's Vertex AI services, including AutoML, AI Notebooks, and AI Platform Pipelines.
Deep dive into prompt engineering techniques, mastering the art of crafting prompts that unlock the full potential of pre-trained language models for various tasks like text generation, question answering, and summarization.
Explore and experiment with different ML architectures and techniques, such as deep learning, natural language processing, and computer vision, to find the optimal solution for specific business needs.
Partner with data scientists, software engineers, and product managers to understand requirements, translate them into technical solutions, and integrate models into production workflows.
Continuously evaluate and monitor model performance, identifying opportunities for improvement and ensuring ethical considerations are addressed.
Stay up-to-date with the latest advancements in AI and ML, actively exploring new tools, techniques, and research findings.
Contribute to internal knowledge sharing and mentor junior team members, fostering a collaborative and learning environment.
Qualifications:
4+ years of experience in AI/ML, with a strong understanding of machine learning concepts and methodologies.
Proven experience developing and deploying ML models using cloud platforms like Google Cloud Platform's Vertex AI.
Familiarity with various machine learning libraries and frameworks (e.G., TensorFlow, PyTorch, scikit-learn).
Experience with prompt engineering techniques and applications in diverse tasks.
Excellent communication and collaboration skills, able to effectively explain complex technical concepts to both technical and non-technical audiences.
Passion for learning and staying up-to-date with the rapidly evolving field of AI/ML.
Bonus Points:
Experience with building and deploying ML models in production environments.
Experience with containerization technologies like Docker and Kubernetes.
Experience with CI/CD pipelines and automation tools.
Public cloud certifications (e.G., Google Cloud Certified Professional Cloud ML Engineer).
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