Machine Learning Engineer - Generative AI & NLP Specialist
December 20
Description
• Design and optimize translation systems leveraging advanced NLP and Generative AI (GenAI) techniques. • Focus on delivering contextually accurate, multilingual solutions with domain-specific customizations to meet diverse client needs. • Continuously improve performance using metrics like BLEU scores and human evaluation benchmarks. • Take ownership of the entire machine learning pipeline, from prototyping and concept validation to scalable production deployment. • Collaborate with cross-functional teams to align solutions with business objectives and ensure seamless integration. • Implement monitoring frameworks to track model performance, detect anomalies, and ensure reliability in production. • Automate pipelines for model retraining and fine-tuning to address data drift and maintain accuracy. • Deploy highly scalable inference endpoints that handle concurrent requests efficiently while maintaining low latency. • Ensure compliance with security standards, including encryption, access control, and API authentication. • Develop well-documented APIs to enable seamless integration of GenAI capabilities into applications and external systems. • Support API versioning and updates to meet evolving requirements. • Work with vector and graph databases to enable efficient Retrieval-Augmented Generation (RAG) systems. • Optimize data retrieval processes and evaluate RAG metrics, such as precision and relevance, to ensure high-quality results.
Requirements
• Deep understanding of the full ML lifecycle, including development, training, deployment, and maintenance. • Proficiency in tools like Weights & Biases (W&B) or MLflow to track and manage experiments. • Strong Python programming skills, with expertise in ML libraries such as LangChain, LlamaIndex, PyTorch, TensorFlow, NumPy, SciPy, pandas, and scikit-learn. • Experience designing APIs with industry best practices. • Strong knowledge of large language models, including open-source and commercial implementations, and their practical applications. • Basic experience in building or deploying AI agents for specialized tasks. • Hands-on experience with vector and graph databases, including understanding metrics for evaluating RAG systems. • Proficiency in cloud platforms, preferably Google Cloud Platform (GCP). • Familiarity with Docker and containerization technologies. • Proven ability to ensure that GenAI deployments are scalable, secure, and efficient.
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