Sr. Machine Learning Engineer — ML/AI Team

A Place for Mom

10d•$120,000 - $135,000•Remote

About The Position

A Place for Mom is seeking a Senior Machine Learning Engineer to design, build, and scale production-grade machine learning and Generative AI systems. This role will focus on developing advanced ML and LLM-powered applications that leverage structured and unstructured data to drive business impact. The ideal candidate combines strong foundations in traditional machine learning with hands-on experience building and deploying Large Language Model (LLM) solutions and prompt-engineered systems in production environments.

Requirements

Master’s degree in Computer Science, Mathematics, or a related field, or equivalent working experience.
5+ years of proven experience as a Machine Learning Engineer, with significant experience working with data from structured and unstructured data sources, ETL processes, and data quality management.
Strong proficiency in SQL, Databricks, AWS services, Python, and Spark.
Experience with ML frameworks such as XGBoost, Scikit-learn, TensorFlow, Keras, or PyTorch.
Hands-on experience building and deploying LLM-powered applications, including prompt engineering, evaluation frameworks, Retrieval-Augmented Generation (RAG), embedding models/vector databases.
Familiarity with MLOps and/or LLMOps tooling and CI/CD workflows.
Excellent analytical and problem-solving skills with a strong ability to derive insights from complex data sets.
Effective communication skills with the ability to convey technical information and business impact to non-technical stakeholders.

Responsibilities

Study and transform data science prototypes using appropriate ML and GenAI architectures.
Design and develop machine learning and LLM-powered systems using modern GenAI architectures (e.g., RAG, prompt engineering, embeddings, vector databases).
Solve complex problems with multilayered data sets and optimize existing machine learning libraries and frameworks.
Construct optimized data pipelines to feed both traditional ML models and LLM-based systems.
Run machine learning tests and experiments and document findings and results.
Implement and monitor model and data quality checks to ensure accuracy and consistency of our models and pipelines in production.
Ensure system reliability, performance optimization, and responsible deployment of ML and LLM solutions in production.
Manage the full model lifecycle, including retraining, performance monitoring, and continuous improvement.
Partner with Product, Engineering and Business stakeholders to translate requirements into scalable AI/ML and GenAI solutions, contributing to system architecture and technical design.
Provide technical guidance and support on AI/ML initiatives, delivering clear, actionable insights to stakeholders.
Contribute to AI engineering standards and best practices across the organization.
Create and maintain comprehensive documentation for ML and GenAI systems, including prompt libraries, evaluation frameworks, and architectural decisions.
Establish and promote best practices across MLOps, LLMOps and AI system governance.