Job Description

Job Description
The role involves developing and optimizing artificial intelligence and machine learning systems, with a special focus on language models (LLM) and natural language processing (NLP). You will be involved in all phases of the models' lifecycle, from building and training to deployment and maintenance in production environments, contributing to scalable and reliable AI solutions.
Responsibilities:
Build and optimize LLM-based systems.
Design, tune and optimize machine learning models, including classic and modern ML approaches.
Experiment with architectures like Transformers and Mixture-of-Experts.
Apply efficient training and tuning techniques, including LoRA and efficient parameter methods.
Develop robust evaluation methods to measure the quality and reliability of the models.
Build and deploy production-ready NLP pipelines and services.
Ensure models operate with low latency and high availability.
Manage the acquisition, processing and governance of large structured and unstructured data sets.
Apply data analysis and validation techniques to improve training pipelines.
Ensure compliance with data privacy and governance standards, especially in financial services.
Contribute to DevOps and MLOps pipelines, implementing version control, testing and CI/CD workflows.
Support the implementation and monitoring of models in production.
Collaborate with product, engineering and data teams to deliver AI solutions.
Stay up to date on AI and NLP research, applying innovative approaches where they add value.
Requirements:
Experience creating, training, and deploying machine learning models.
Skills in Python and experience with NumPy, Pandas, SciPy, and modern ML frameworks like PyTorch or TensorFlow.
Experience working with large volumes of unstructured data.
Experience building and implementing production-ready NLP systems.
Familiarity with API integrations and data acquisition pipelines.
Experience in software engineering, including Git and agile development.
Experience in cloud environments, preferably AWS.
Experience with containers and orchestration such as Docker or Kubernetes (recommended).
Knowledge of frameworks such as vLLM or NeMo (recommended).
Knowledge of NLP applications in financial services (recommended).
Experience in designing evaluation methodologies for LLM results (recommended).
Experience in creating intelligent agents or multi-agent systems.

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