4 months ago
Remote, United StatesSenior
Responsibilities
- Lead end-to-end machine learning initiatives focused on NLP-heavy applications, from problem framing and data exploration through model development, evaluation, and improvement.
- Develop and maintain Python-based machine learning pipelines and experimentation workflows.
- Build and integrate generative AI and agentic solutions using frameworks such as LangChain and CrewAI.
- Define evaluation criteria and conduct structured model and LLM evaluations using appropriate frameworks and metrics.
- Use Databricks and cloud provider services to manage data and scale experimentation and training workflows.
- Develop graph-based solutions, ontologies, context graphs, agent memory management, and dynamic context compression techniques.
Requirements
- Strong applied experience in data science and machine learning, including developing, evaluating, and iterating on production-oriented models.
- Demonstrated ability to build AI/ML solutions using Python.
- Hands-on experience with AI application frameworks such as LangChain or CrewAI.
- Practical experience applying generative AI techniques and patterns in real-world applications.
- Experience with graph-based solutions such as Neo4j for ontologies and context graphs for AI agents.
- Understanding of agentic harness development, including memory management and dynamic context compression.
- Experience developing and improving NLP-focused machine learning solutions.
- Ability to work in a Databricks-based environment for model development and experimentation.
- Experience with Apache Spark is preferred.
- Experience with LLM evaluation frameworks such as LangSmith is preferred.
Tech Stack
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