Senior Machine Learning Engineer – RAGs

Factor Funding Company

. Design, develop, and optimize Retrieval-Augmented Generation (RAG) models for complex, real-world client problems.

Posted 10/31/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying Retrieval-Augmented Generation (RAG) models and large language models (LLMs) while collaborating with cross-functional teams to deliver tailored AI solutions. Proficient in writing production-level code and optimizing machine learning models for real-world applications.

Highest-signal resume keywords

Retrieval-Augmented Generation (RAG)Large Language Models (LLMs)Machine Learning Model DeploymentPython ProgrammingNLP and Deep Learning Frameworks

ATS Keywords

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Hard Skills

Machine LearningDeep LearningNatural Language Processing (NLP)Model OptimizationAlgorithm DevelopmentFine-TuningPrompting TechniquesProduction-Level Code WritingScalable Code DevelopmentTestable Code

Soft Skills

CollaborationTechnical LeadershipUser Experience FocusCustomer Needs Prioritization

Tools & Technologies

PyTorchTensorFlowLangChainAWSGCP

Industry Keywords

AI SolutionsMachine Learning InfrastructureTransformative AI/ML OpportunitiesGenAI Product Frameworks

Tech Stack

Tools & technologies

AWSCloudGoogle Cloud PlatformPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Design, develop, and optimize Retrieval-Augmented Generation (RAG) models for complex, real-world client problems.
Improve RAG model performance through advanced algorithms, innovative techniques, and fine-tuning.
Collaborate with client Data and Engineering teams to build robust machine learning infrastructure.
Work with client leadership to identify and leverage transformative AI/ML opportunities.
Fine-tune and adapt large language models for specific tasks and domains within the RAG framework.
Deploy RAG models into production environments and ensure seamless integration and long-term success.
Apply advanced machine learning techniques, including LLMs, to develop client-tailored AI solutions.
Write clean, maintainable, scalable, documented, and testable code.
Prioritize user experience and customer needs in product development.
Design and develop GenAI product frameworks, including search interfaces, chatbots, and summarization tools.
Build and implement machine learning models and algorithms supporting client growth and success.
Provide technical leadership in identifying and evaluating AI/ML opportunities.

Requirements

What you’ll need

5+ years of hands-on experience developing and deploying machine learning models in production environments.
4+ years of experience with production NLP and deep learning models using frameworks like PyTorch and TensorFlow.
At least 1+ year of experience with Retrieval-Augmented Generation (RAG) and other advanced techniques to optimize model performance.
Proven experience writing production-level code, with strong proficiency in Python.
Expertise in working with large language models (LLMs) such as GPT, Gemini, and Claude, along with proficiency in LLM frameworks like LangChain.
Strong understanding of prompting techniques, and the trade-offs between prompting and fine-tuning.
Experience with cloud platforms such as AWS or GCP (AWS preferred), or equivalent on-premise platforms.

Benefits

Comp & perks

Ownership through equity participation.
Annual company retreat.
Education bonus for continuous learning.
Company-wide winter break.
Paid time off.
Optional in-person events and meetups.
Tailored career roadmaps.
High-performance culture.