Performance Outcomes

• Modeling & Data Science

• Develop predictive and generative ML models to support acquisition, retention, personalization, and member engagement.

• Fine-tune and adapt Large Language Models (LLMs) for LegalShield-specific use cases, including call transcript analysis, voice-of-customer workflows, and content generation.

• Conduct experimentation, model evaluation, and rigorous validation to ensure accuracy, fairness, and business alignment.

• Partner with business teams to design experiments, analyze results, and translate insights into actionable strategies.

• Platform & Productionization

• Build and maintain infrastructure for deploying and scaling ML/GenAI models.

• Develop APIs, MCP servers and microservices to serve models across internal and external applications.

• Translate prototypes into production-grade systems for business and member-facing teams.

• Implement CI/CD pipelines, containerization, and orchestration (Docker, Kubernetes).

• Deploy and manage ML workflow orchestration, feature stores, and model registries.

• Monitor and optimize performance, drift, latency, and cloud costs.

• Ensure compliance with SOC2, HIPAA, GDPR, and other data privacy/security standards.

• AI Tooling & Applications

• Develop internal AI tools that improve productivity and deliver measurable cost savings.

• Develop external, member-facing AI-powered applications that create new value and enhance customer experience.

• Build reusable platforms and tools that accelerate AI/ML adoption across LegalShield.

Education, Knowledge, and Experience

Required: 

• Advanced degree in Data Science, Statistics, Computer Science, or a related field (or equivalent practical experience). 

• 7+ years’ experience in software engineering, Machine Learning, or Applied Data Science (Python preferred; APIs, microservices, distributed systems). 

• Proven ability to take AI/ML solutions from prototype to production and scale adoption across teams.

• Hands-on experience with Large Language Models (LLMs) and retrieval-augmented generation (RAG) pipelines, LLM Fine-tuning and training. 

• Familiarity with ML infrastructure: feature stores, model registries, or workflow orchestration tools.

• Experience with CI/CD, Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure).

• Understanding of compliance and data privacy standards, and ability to apply them in AI systems.

• Proven track record of deploying and operating ML/AI systems in production.

Preferred:

• Deep knowledge of ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain).

• Experience building shared AI/ML platforms or internal developer tools.

• Familiarity with observability and Cataloging tools (Snowflake, Secoda, Airflow).

• Prior experience working cross-functionally with data science, infrastructure, and product teams.

Performance Outcomes:

• Successful development and maintenance of data systems and solutions, as described in the responsibilities above

• Successful engagement and collaboration with others:

• Work closely and effectively with teammates, which may include data engineers, software engineers, program/product managers, software test engineers, and others

• Contribute to the implementation of dynamic requirements/initiatives with teammates, cross-team collaborators, and with business partners as appropriate

FLSA Status

Exempt

Physical Requirements/ Work Environment

The work environment characteristics and physical demands described here are representative of these an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 

• Prolonged periods of sitting or standing at a desk 

• Extensive periods of working on a computer