Applied Machine Learning Engineer

Role: Applied Machine Learning Engineer

Location:

Your Role

At BIG Language Solutions, we're shaping the future of multilingual communication through cutting-edge AI. As an Applied Machine Learning Engineer, youll be instrumental in designing, building, and scaling ML-driven capabilities that enhance our translation, localization, and linguistic intelligence products. You will work across data science, engineering, and product teams to bring research into production and help us automate, augment, and accelerate human translation.

This role is perfect for someone who thrives on experimentation, iteration, and deployment of ML models in real-world systems.

You Will

• Adapt and fine-tune open-source LLMs and implement Retrieval-Augmented Generation (RAG) pipelines to perform specialized tasks, enhancing translation quality and workflow efficiency.
• Design, deploy, and optimize AI agents capable of enterprise-grade conversations, integrating them with internal systems via APIs, databases, and automation tools.
• Develop evaluation pipelines for LLM performance on multilingual tasks, and guide model selection or fine-tuning accordingly.
• Support the prototyping and productionization of AI-driven features that support localization workflows, with a focus on delivering scalable impact across translation and interpretation processes.
• Collaborate with cross-functional teams (engineering, product, and operations) to shape BIG Language AI’s roadmap
• Stay current with research in applied ML, especially around LLMs, sequence modeling, and cross-lingual representations.

Requirements

• 3+ years of experience in applied Machine Learning, preferably with a focus on NLP or multilingual AI.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Computational Linguistics, or related field.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or HuggingFace Transformers.
• Experience building, training, evaluating, and deploying models in production environments.
• Strong understanding of NLP concepts such as information retreival, sequence modeling, NER, and machine translation.
• Experience with MLOps tools (e.g., MLflow, Weights & Biases, Docker, Kubernetes) is a strong plus.
• Excellent problem-solving skills and ability to work cross-functionally with product, data, and engineering teams.