GenAI Data Engineer

DCV Technologies
London, United Kingdom
Today
£40,000 – £60,000 pa

Salary

£40,000 – £60,000 pa

Job Type
Contract
Work Location
Hybrid
Seniority
Mid
Education
Degree
Posted
28 Apr 2026 (Today)

Position: GenAI Data Engineer

Location: London or Edinburgh (Hybrid-2 days a week from office)

6 months contract position

Your responsibilities:

* Design and maintain scalable data pipelines using PySpark, Python, and distributed computing frameworks to support high‑volume data processing.

* Architect and optimize 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

Your Profile

Essential skills/knowledge/experience:

* 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 Gen AI/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 optimization techniques (quantization, distillation, inference optimization).

* Strong capability to debug, tune, and optimize distributed systems and AI pipelines.

* Desirable skills/knowledge/experience: (As applicable)

* Pyspark, Python, SQL, AWS, Gen AI

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