About the Role:We are seeking a highly skilled and experienced Data Engineering to join our growing team. As a Data Engineering, you will play a critical role in designing, building, and scaling Google's massive data infrastructure and platforms. You will be a technical leader and mentor, driving innovation and ensuring the highest standards of data quality, reliability, and performance.Responsibilities:Design and Architecture:Design and implement scalable, reliable, and efficient data pipelines and architectures for various Google products and services.Develop and maintain data models, schemas, and ontologies to support diverse data sources and use cases.Evaluate and recommend new and emerging data technologies and tools to improve Google's data infrastructure.Collaborate with product managers, engineers, and researchers to define data requirements and translate them into technical solutions.Data Processing and Pipelines:Build and optimize batch and real-time data pipelines using Google Cloud Platform (GCP) services such as Dataflow, Dataproc, Pub/Sub, and Cloud Functions.Develop and implement data quality checks and validation processes to ensure data accuracy and consistency.Design and implement data governance policies and procedures to ensure data security and compliance.Data Storage and Management:Design and implement scalable data storage solutions using GCP services such as BigQuery, Cloud Storage, and Spanner.Optimize data storage and retrieval for performance and cost-effectiveness.Implement data lifecycle management policies and procedures.Team Leadership and Mentorship:Provide technical leadership and guidance to data engineers and other team members.Mentor and coach junior engineers to develop their skills and expertise.Foster a culture of innovation and collaboration within the team.Qualifications:Bachelor's or Master's degree in Computer Science, Engineering, or a related field.8+ years of experience in data engineering or a related field.Strong understanding of data warehousing, data modeling, and ETL processes.Expertise in designing and implementing large-scale data pipelines and architectures.Proficiency in SQL and at least one programming language such as Python or Java.Experience with Google Cloud Platform (GCP) services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.Experience with open-source data processing frameworks such as Hadoop, Spark, and Kafka.Excellent communication, interpersonal, and collaboration skills.Preferred Qualifications:Experience with data governance and data quality management.Experience with machine learning and data science.Experience with containerization and orchestration technologies such as Docker and Kubernetes.Contributions to open-source projects or communities.Google Cloud Professional Data Engineer certification.
As a highly skilled and innovative Generative AI at Google, you will lead the design, development, and implementation of cutting-edge AI solutions. Your role will involve driving the adoption and advancement of generative AI across various Google products, services, and research initiatives. To excel in this position, you must possess a deep understanding of AI/ML principles, strong engineering skills, and a passion for pushing the boundaries of generative AI capabilities. Your responsibilities will include providing technical leadership in the design, development, and deployment of generative AI solutions. You will need to stay updated on the latest research and advancements in generative AI, identify and evaluate new technologies and tools, and design scalable and reliable architectures for generative AI systems. Additionally, you will lead the development and training of state-of-the-art generative AI models, optimize model performance, and implement responsible AI practices such as fairness, bias detection, and explainability. Collaboration and mentorship are crucial aspects of this role. You will collaborate with researchers, engineers, and product managers, mentor other engineers on generative AI best practices, and contribute to Google's AI community and thought leadership. To qualify for this position, you should have a PhD or Master's degree in Computer Science, Artificial Intelligence, or a related field, a strong understanding of deep learning, natural language processing, and/or computer vision, expertise in generative AI models and frameworks, proficiency in Python and relevant AI/ML libraries, experience with large-scale data processing and distributed systems, as well as excellent communication, interpersonal, and collaboration skills. Preferred qualifications include experience with Google Cloud Platform (GCP) and its AI/ML services, publications or patents in generative AI, contributions to open-source AI projects, and experience with AI ethics and responsible AI development.,
Experience - 7 to 10 years Role Description: Evonence is looking for Generative AI practice head to drive end-to-end AI solution development from architecture and model training to responsible deployment and mentorship. The role balances research, engineering, and leadership, making it ideal for someone with a strong academic background and hands-on experience in production-level AI systems. Key Responsibilities: Lead AI initiatives and drive innovation in generative models. Evaluate and adopt cutting-edge AI trends (e.g., from academic papers, NeurIPS, CVPR). Be a go-to authority for generative AI direction. Design production-grade AI systems using frameworks like transformers, GANs, or diffusion models. Focus on performance, scalability, and security, suggesting deployment on distributed platforms. Build and fine-tune large-scale models. Embed Responsible AI practices: mitigate bias, improve transparency. Act as a bridge between research and product teams. Mentor junior engineers, encourage cross-team collaboration, and shape internal AI strategy. Required Skills: Advanced degree (PhD or Master's) in AI/ML or related fields. Expertise in transformer architectures, GANs, diffusion models. Experience with Python, TensorFlow, JAX, and distributed computing. Solid grasp of natural language processing, computer vision, or multi-modal AI. Good to Have: Experience on Google Cloud Platform (GCP). Publications or patents in top-tier AI venues. Contributions to open-source tools like HuggingFace Transformers, TensorFlow, or Diffusers. Knowledge of AI ethics frameworks and tools like Fairness Indicators, Explainable AI (XAI).