Principal Data Engineer for Enterprise Ai/ml
hace 4 semanas
**Responsibilities**:- Design, develop, and implement data pipelines for ingesting, pre-processing, and transforming diverse data types (html, Image,.pdf, Audio, video) for Generative AI model training and inference.- Engineer data for vector databases (e.g., Pinecone, Redis, Chroma) and Large Language Models (LLM, GPT-4, Claude 2.0) and for tasks like text summarization, entity extraction, and classification.- Build and maintain efficient data storage solutions, including data lakes, warehouses, and databases suitable for large-scale generative AI datasets. Implement data security and governance policies to safeguard the privacy and integrity of sensitive data used in Generative AI projects.- Collaborate with data scientists and engineers to understand data requirements for Generative AI models and translate them into efficient data pipelines.- Monitor and optimize data pipelines for performance, scalability, and cost-effectiveness.- Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics, including operational efficiency and customer acquisition.- Work with stakeholders, including data, design, product, and executive teams, and assisting them with data-related technical issues- Collaborate with stakeholders, including the Executive, Product, Data, and Design teams, to support their data infrastructure needs while assisting with data-related technical issues.**Qualifications**:- Bachelor's degree in computer science, data science, statistics, or a related field, or equivalent experience.- 6+ years of proven experience in data engineering, ETL, SQL, database, JSON data, data pipeline development, building data platforms, and data storage technologies.- 2+ years of experience building and maintaining data pipelines for machine learning projects.- Strong understanding of data structures, data modeling principles, data quality measures, and data security best practices, with experience in transforming, cleaning, and organizing unstructured data- High proficiency in Python, SQL, and scripting languages.- Experience in continuous integration/deployments for large data pipelines and familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for scalable and efficient model deployment.- Familiarity with implementing data and/or machine learning algorithms in production systems (e.g. AWS Sagemaker, GCP Datalab, or custom implementation);- Hands-on experience with cloud platforms (e.g., OCI, AWS, GCP, Azure) for data storage and processing, along with Gen AI services like OCI Gen AI, Azure AI Services, or AWS BedRock.- Strong problem-solving skills and the ability to analyze data and design solutions to complex data issues.- Familiarity with modern ETL stack (Airflow, DBT, Snowflake), data stream frameworks (Kafka, Kinesis), vector databases (e.g., Pinecone, Redis, Chroma) and OpenSearch / Elasticsearch.- Understanding of Large Language Models (LLM, GPT-4, Claude 2.0) for tasks like text summarization, entity extraction, and classification.- Excellent communication skills and the ability to convey complex technical concepts to non-technical stakeholders.- Ability to work independently and collaboratively in a fast-paced environment.- Practical knowledge of Agile project management and software development methodologies such as Scrum and SAFe.- Experiencing working with globally distributed teams.
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