Language Engineer

Job Title: Language Engineer

Location:

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About the Role:

Language Engineer with a strong expertise in Generative AI

This is a collaborative role where linguistic intuition meets deep technology. You’ll work alongside machine learning scientists, applied researchers, and product engineers to shape next-gen GenAI experiences.

Key Responsibilities:

• Design and execute linguistic experiments to evaluate and improve generative models across multiple languages and tasks.
• Contribute to prompt engineering, fine-tuning, and evaluation of LLMs for conversational AI, summarization, translation, and more.
• Curate, annotate, and manage high-quality language datasets to support model training and benchmarking.
• Perform linguistic error analysis and develop rule-based or data-driven corrections for model outputs.
• Collaborate with research teams to publish internal findings, whitepapers, or contribute to academic papers.
• Support multilingual and cross-cultural GenAI development by ensuring linguistic and cultural nuance in outputs.
• Prototype and implement linguistic modules/tools to automate analysis and annotation workflows.
• Monitor emerging trends in computational linguistics, foundation models, and GenAI.

Required Qualifications:

• Proven experience working with NLP/LLM models (e.g., GPT, BERT, T5, LLaMA, Claude, Gemini).
• Solid understanding of syntax, semantics, discourse, and pragmatics.
• Strong programming/scripting skills in Python and experience with NLP libraries (e.g., spaCy, Hugging Face Transformers, NLTK).
• Experience in designing or executing linguistic evaluations and A/B tests for AI models.
• Familiarity with large-scale data annotation, QA/QC processes, and labeling tools.

Preferred Qualifications:

• Experience in prompt engineering and GenAI evaluation techniques.
• Experience with multilingual or low-resource language support in NLP systems.
• Strong research publication record or contributions to open-source NLP/GenAI projects.
• Familiarity with MLOps tools, vector databases, LLMOps, and retrieval-augmented generation (RAG).
• Ability to interpret and distill model behaviors into actionable insights for research teams.

What You’ll Get:

• A chance to influence GenAI experiences and multilingual LLM development at scale.
• Exposure to state-of-the-art AI systems, tools, and global NLP challenges.
• A supportive and intellectually stimulating work environment.