Linguistc AI & ML Engineer

ROLE OVERVIEW

The Machine Learning Engineer – Generative AI & NLP Specialist will design, develop, and implement cutting-edge AI-driven systems with a strong focus on linguistics and language technology. This role will enhance translation systems using advanced Natural Language Processing (NLP) techniques and Generative AI (GenAI). The ideal candidate will have extensive experience in computational linguistics, machine translation (MT), and multilingual AI models, alongside expertise in end-to-end machine learning (ML) lifecycles, large language models (LLMs), and scalable AI solutions.

KEY RESPONSIBILITIES

- Design and optimize translation systems leveraging advanced NLP, Generative AI (GenAI), and linguistic expertise.

- Apply linguistic principles and computational linguistics techniques to enhance model performance, particularly in multilingual and domain-specific contexts.

- Ensure contextual accuracy and linguistic coherence in AI-driven translation models.

- Improve performance using linguistic quality metrics like BLEU, TER, METEOR, and human evaluation benchmarks.

- Take ownership of the entire machine learning pipeline, from prototyping and concept validation to scalable production deployment.

- Collaborate with linguists, translators, and domain experts to refine AI models for specific industry applications.

- Implement monitoring frameworks to track linguistic model performance, detect anomalies, and ensure translation reliability.

- Automate pipelines for model retraining, fine-tuning, and adaptation to handle language evolution and domain-specific needs.

- Deploy highly scalable multilingual inference endpoints, ensuring low-latency and high-accuracy responses.

- Ensure compliance with data security, linguistic data privacy, and ethical AI standards.

- Develop well-documented APIs to enable seamless integration of GenAI-powered linguistic models into applications and external systems.

- Optimize data retrieval and adaptation for multilingual NLP models, evaluating Retrieval-Augmented Generation (RAG) metrics such as precision and relevance.

REQUIREMENTS

- Background in computational linguistics, natural language processing (NLP), or language technology.

- Deep understanding of the full ML lifecycle, including development, training, deployment, and maintenance.

- Experience working with machine translation (MT), speech-to-text, text-to-speech, and multilingual AI models.

- Strong Python programming skills, with expertise in ML libraries such as LangChain, LlamaIndex, PyTorch, TensorFlow, NumPy, SciPy, pandas, and scikit-learn.

- Proficiency in linguistic annotation, tokenization, stemming, lemmatization, and text normalization techniques.

- Hands-on experience working with BLEU, METEOR, TER, and other linguistic quality metrics to evaluate translation accuracy.

- Experience designing APIs for multilingual NLP applications with industry best practices.

- Strong knowledge of large language models (LLMs), including open-source and commercial implementations, and their linguistic applications.

- Familiarity with vector and graph databases to support multilingual Retrieval-Augmented Generation (RAG) systems.

- Proficiency in cloud platforms, preferably Google Cloud Platform (GCP).

- Experience with Docker, containerization, and deployment of AI-powered linguistic models.

- Proven ability to ensure that GenAI deployments are scalable, secure, and linguistically robust.

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