GenAI Data Engineer || 6 Months Extendable Contract || Remote

Job Description

As the data engineering consultant, you should have the common traits and capabilities that are listed Essential Requirements and meet many of the capabilities listed in Desirable Requirements Essential Requirements and Skills 10+ years working with customers in the Data Analytics, Big Data and Data Warehousing field. 10+ years working with data modeling tools. 5+ years building data pipelines for large customers. 2+ years of experience working in the field of Artificial Intelligence that leverages Big Data. This should be in a customer-facing services delivery role. 3+ years of experience in Big Data database design. A good understanding of LLMs, prompt engineering, fine tuning and training. Strong knowledge of SQL, NoSQL and Vector databases. Experience with popular enterprise databases such as SQL Server, MySQL, Postgres and Redis is a must. Additionally experience with popular Vector Databases such as PGVector, Milvus and Elasticsearch is a requirement. Experience with major data warehousing providers such as Teradata. Experience with data lake tools such as Databricks, Snowflake and Starburst. Proven experience building data pipelines and ETLs for both data transformation and multiple data source data extraction. Experience with automation of the deployment and execution of these pipelines. Experience with tools such as Apache Spark, Apache Hadoop, Informatica and similar data processing tools. Proficient knowledge of Python and SQL is a must. Proven experience with building test procedures, ensuring the quality, reliability, performance, and scalability of the data pipelines. Ability to develop applications that expose Restful APIs for data querying and ingestion. Experience preparing training data for Large Language Model ingestion and training (e.g. through vector databases). Experience with integrating with RAG solutions and leveraging related tools such as Nvidia Guardrails. Ability to define and implement metrics for RAG solutions. Understanding of typical AI tooling ecosystem including knowledge and experience of Kubernetes, MLOps, LLMOps and AIOps tools. Ability to gain customer trust, ability to plan, organize and drive customer workshops. Good communication skills in English is a must. The ability to work in a highly efficient team using an Agile methodology such as Scrum or Kanban. Ability to have extended pairing sessions with customers, enabling knowledge transfers in complex domains. Ability to influence and interact with confidence and credibility at all levels within the Dell Technologies companies and with our customers, partners, and vendors. Experience working on project teams within a defined methodology while adhering to margin, planning and SOW requirements. Ability to be onsite during customer workshops and enablement sessions. Desirable Requirements and Skills Knowledge of industry widespread AI Studios and AI Workbenches is a plus. Experience building and using Information Retrieval (IR) frameworks to support LLM inferencing. Working knowledge of Linux is a plus. Knowledge of using Minio is appreciated. Experience using Lean and Iterative Deployment Methodologies. Working knowledge of cloud technologies is a plus. University Degree aligned to Data Engineering is a plus. In possession of relevant industry certifications e.g. Databricks Certified Data Engineer, Microsoft Certifications, etc.