Senior ML Engineer (NLP, LLM)

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

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