ROLE OVERVIEW
The Machine Learning Engineer – Generative AI & NLP Specialist to design, develop, and implement cutting-edge AI-driven systems. This role will focus on enhancing translation systems using advanced NLP techniques and Generative AI (GenAI). The ideal candidate will have extensive experience in end-to-end machine learning (ML) lifecycles, large language models (LLMs), and the ability to create scalable, secure, and efficient AI solutions.
KEY RESPONSIBILITIES
- 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.