AI Data Strategy Engineer – Applied Scientist, LLM Data

Jobtailor; Deutschland; Vor Ort; EUR 70.000 - 100.000; Vollzeit

Zusammenfassung

Jobtailor is seeking a Senior AI Data Engineer to define end-to-end data roadmaps for multilingual and multimodal AI systems, covering text, speech, translation, and agentic workflows.

You will design dataset curation pipelines, create annotation schemas, QA rubrics, and reviewer workflows for diverse data domains, collaborating with ML engineers, linguists, and product teams. Proficiency with Python, SQL, and AWS data tools is required.

Qualifikationen

• Bachelor’s degree in Computer Science, Machine Learning, Data Science, Computational Linguistics, Linguistics, Statistics, or a related field, or equivalent practical experience.
• 4+ years of experience in AI data, ML data operations, NLP data engineering, applied ML, speech/translation data, or LLM data workflows.
• Strong hands‑on experience with Python, SQL, and dataset curation pipelines.
• Experience with annotation workflows, QA rubrics, evaluation datasets, or human‑in‑the‑loop data processes.
• Familiarity with multilingual NLP, speech data, translation data, low‑resource languages, conversational AI, or agentic AI datasets.
• Working knowledge of AWS data and ML tools such as S3, Glue, SageMaker, Bedrock, Lambda, Step Functions, EKS/ECS, IAM, or KMS.
• Strong communication skills and ability to work with ML engineers, applied scientists, product teams, linguists, data teams, and vendors.

Aufgaben

• Define the end-to-end data roadmap for multilingual and multimodal AI systems, including text, speech, translation, interpretation, low-resource languages, and agentic AI workflows.
• Design and build dataset curation pipelines for training, post‑training, and evaluation, including cleaning, deduplication, filtering, PII redaction, quality scoring, sampling, balancing, and versioning.
• Create annotation schemas, labeling guidelines, QA rubrics, golden datasets, and reviewer workflows for multilingual, speech, translation, and agentic AI data.
• Build evaluation datasets and benchmarks, analyze model failure modes, and translate performance gaps into targeted data improvements.
• Support post‑training data workflows such as SFT, instruction tuning, preference data, RLHF/DPO‑style data, reward model data, and synthetic data generation.
• Use modern annotation tools and AWS‑based data infrastructure to scale secure, traceable, and compliant AI data workflows.

Kenntnisse

Python

SQL

Dataset curation pipelines

Communication skills

Ausbildung

Bachelor's degree in Computer Science or related field

Tools

S3

Glue

SageMaker

Bedrock

Lambda

Step Functions

EKS/ECS

IAM

KMS

Jobbeschreibung

Responsibilities

• Define the end-to-end data roadmap for multilingual and multimodal AI systems, including text, speech, translation, interpretation, low-resource languages, and agentic AI workflows.
• Design and build dataset curation pipelines for training, post‑training, and evaluation, including cleaning, deduplication, filtering, PII redaction, quality scoring, sampling, balancing, and versioning.
• Create annotation schemas, labeling guidelines, QA rubrics, golden datasets, and reviewer workflows for multilingual, speech, translation, and agentic AI data.
• Build evaluation datasets and benchmarks, analyze model failure modes, and translate performance gaps into targeted data improvements.
• Support post‑training data workflows such as SFT, instruction tuning, preference data, RLHF/DPO‑style data, reward model data, and synthetic data generation.
• Use modern annotation tools and AWS‑based data infrastructure to scale secure, traceable, and compliant AI data workflows.

Requirements

• Bachelor’s degree in Computer Science, Machine Learning, Data Science, Computational Linguistics, Linguistics, Statistics, or a related field, or equivalent practical experience.
• 4+ years of experience in AI data, ML data operations, NLP data engineering, applied ML, speech/translation data, or LLM data workflows.
• Strong hands‑on experience with Python, SQL, and dataset curation pipelines.
• Experience with annotation workflows, QA rubrics, evaluation datasets, or human‑in‑the‑loop data processes.
• Familiarity with multilingual NLP, speech data, translation data, low‑resource languages, conversational AI, or agentic AI datasets.
• Working knowledge of AWS data and ML tools such as S3, Glue, SageMaker, Bedrock, Lambda, Step Functions, EKS/ECS, IAM, or KMS.
• Strong communication skills and ability to work with ML engineers, applied scientists, product teams, linguists, data teams, and vendors.

Core Competencies

Demonstrates expertise in defining data roadmaps for multilingual and multimodal AI systems, with a strong focus on dataset curation, evaluation, and annotation workflows. Proficient in utilizing AWS data tools and programming languages to enhance AI data operations and ensure compliance.