AI Data Strategy Engineer – Applied Scientist, LLM Data

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.