Offer summary

Qualifications:

Proven track record in machine learning or related fields, including publications or patents., Deep understanding of Natural Language Processing (NLP) and expertise in large language models (LLMs)., Strong grasp of mathematical and statistical concepts relevant to machine learning., Hands-on experience with model evaluation and deployment in production environments..

Key responsabilities:

Lead end-to-end data science efforts, focusing on ML engineering and MLOps tasks.

Develop, fine-tune, and implement ML models, particularly LLMs, across various business domains.

Measure, analyze, and evaluate models in both development and production environments.

Create proof-of-concept models and translate them into production-grade services as they mature.

Job description

Description

We are seeking a highly skilled and motivated Data Scientist with expertise in machine learning (ML), model development, and MLOps to join our team. The ideal candidate will take ownership of end-to-end data science workflows, from model development and fine-tuning to deployment and production. You will work across various business domains, contributing to the design and implementation of cutting-edge ML models, including large language models (LLMs).

Key Responsibilities:

Lead end-to-end data science efforts, including ML engineering and MLOps tasks (approximately 50% of role)
Develop, fine-tune, and implement ML models, particularly LLMs, across various business domains
Measure, analyze, and evaluate models both in development and in production environments
Aggregate datasets from diverse domains to build robust, high-quality datasets
Perform context engineering to enhance model performance and applicability
Create proof-of-concept (POC) models across different business domains
Translate POC models into production-grade services as the POCs mature and gain stability

Requirements

Ideal Candidate Profile: The ideal candidate will bring a mix of academic excellence, hands-on experience, and technical expertise. You will thrive in a dynamic environment and possess the following qualifications:

Proven track record of excellence in ML or related fields (e.g., publications, patents, etc.)
Deep understanding of Natural Language Processing (NLP), with specific expertise in large language models (LLMs)
Strong grasp of mathematical and statistical concepts relevant to machine learning
Experience with model evaluation, particularly in a production setting (preferred)
Hands-on experience with models in production environments (strong advantage)
Expertise in MLOps, including model deployment, monitoring, and maintenance (significant advantage)

Technical Skill Set:

Programming Languages: Python, SQL
LLMs: GPT family, Claude family
Machine Learning Frameworks: PyTorch, TensorFlow
Data Frameworks: Numpy, Pandas
Pipeline Frameworks (advantageous): Hugging Face, Langchain, WanDB, Traceloop, DeepChecks
Vector Database Frameworks: Pinecone, QDrant, Faiss, PGVector, or similar
Cloud Ecosystem Proficiency (advantageous):AWS (e.g., Bedrock, SageMaker, Knowledgebase)
GCP/Azure