GenAI Data Engineer
Our client, a leading global supplier for IT services, requires experienced GenAI Data Engineer to be based at their client’s offices in London or Edinburgh.
Se trata de un puesto híbrido: puedes trabajar a distancia en el Reino Unido y acudir a la oficina de Londres o Edimburgo dos días a la semana.
Se trata de un contrato temporal de más de 6 meses que comenzará lo antes posible.
Tarifa diaria: Tarifa competitiva del mercado
Responsabilidades principales:
- Design and maintain scalable data pipelines using PySpark, Python, and distributed computing frameworks to support high‑volume data processing.
- Architect and optimise AWS-based data and AI infrastructure, ensuring secure, performant, and cost‑efficient ingestion, transformation, and storage.
- Develop, finetune, benchmark, and evaluate GenAI/LLM models, including custom training and inference optimization.
- Implement and maintain RAG pipelines, vector databases, and document-processing workflows for enterprise GenAI applications.
- Build reusable frameworks for prompt management, evaluation, and GenAI operations.
- Collaborate with cross-functional teams to integrate GenAI capabilities into production systems and ensure high-quality data, governance, and operational reliability
Requisitos clave:
- Strong experience with PySpark, distributed data processing, and largescale ETL/ELT pipelines.
- Strong SQL expertise including star/snowflake schema design, indexing strategies, writing optimized queries, and implementing CDC, SCD Type 1/2/3 patterns for reliable data warehousing.
- Advanced proficiency in Python for data engineering, automation, and ML/GenAI integration.
- Hands on expertise with AWS services (S3, Glue, Lambda, EMR, Bedrock / custom model hosting).
- Practical experience with GenAI/LLM model creation, finetuning, benchmarking, and evaluation.
- Solid understanding of RAG architectures, embeddings, vector stores, and LLM evaluation methods.
- Experience working with structured and unstructured datasets (documents, logs, text, images).
- Familiarity with scalable data storage solutions (Delta Lake, Parquet, Redshift, DynamoDB).
- Understanding model optimisation techniques (quantisation, distillation, inference optimisation).
- Strong capability to debug, tune, and optimise distributed systems and AI pipelines.
Debido al volumen de solicitudes recibidas, lamentablemente no podemos responder a todas ellas.
Si no recibe respuesta por nuestra parte en un plazo de 7 días tras enviar su solicitud, considere que, en esta ocasión, su solicitud no ha sido aceptada.
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