with deep expertise in NLP, LLMs, and generative AI. The ideal candidate will have strong experience applying machine learning in an industry setting, working with structured and unstructured data, and deploying models in production environments.

Key Responsibilities:

Conduct experiments and develop prototypes to extract meaningful insights from text data sources of varying sizes.

Collaborate with data scientists, quantitative analysts, data engineers, and MLOps engineers to build end-to-end pipelines that deliver business value.

Train, fine-tune, and deploy
pre-trained machine learning models
in production.

Develop methods for
monitoring and updating models
with new data or state-of-the-art pre-trained models.

Assist with
documentation and governance
of ML and NLP pipeline artifacts.

Innovate solutions that enhance
automation and efficiency
in AI workflows.

Refactor and productionize
research code, models, and data
to meet production standards.

Required Qualifications:

5+ years of experience
applying machine learning in an industry setting.

PhD or MS
in Computer Science, Statistics, or a related field.

Expertise in
machine learning algorithms and frameworks
.

Strong experience in
NLP, LLMs, and generative AI
.

Experience working with both
structured and unstructured data
, particularly
text data
.

Proficiency in
Python
with experience developing production-quality Python modules.

Hands-on experience deploying models in
production environments
.

Strong teamwork and communication skills in
English
.

Preferred Qualifications:

Experience working with
cross-functional teams
.

Contributions to
open-source machine learning projects
.

Background in
causal inference
.

Experience with
investment management use cases
.

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