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# Lead Machine Learning Engineer

- **Company:** NOBLE, INC.
- **Location:** Houston, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $190,000.0 - $205,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Microsoft Azure, Computer Programming, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Reinforcement Learning, Chatbots, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Git, Information Technology, Machine Learning Operations, Software Version Control, Docker, Web Api, Microservices
- **Published:** July 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f2051fdbfb69e652

## About the Role

* Design domain specific AI systems and chatbots capable of complex dialogue management and workflow execution via tools, API calls and multi step tasks based on user goals, multi-agent orchestration., * MSc (preferred) or BSc in Computer Science, Artificial Intelligence, or a related quantitative field.
 * 5+ years of hands-on experience building and deploying AI/ML systems, with a strong focus on Natural Language Processing (NLP).
 * Proven experience designing and shipping chatbots, virtual assistants, or agentic systems using modern LLM-based architectures.
 * Strong programming proficiency in Python (5+ years) and deep experience with core ML/NLP libraries such as PyTorch, TensorFlow
 * Hands-on experience with LLM agent frameworks for building complex, tool-using applications, multi-agent orchestration, or establishing MCP services for platform capabilities.
 * Demonstrated experience with Retrieval-Augmented Generation (RAG), including the use of vector databases like Pinecone, Weaviate, or ChromaDB.
 * Familiarity with techniques for fine-tuning LLMs (e.g., LoRA/QLoRA) and experience working with open-source models (e.g., Llama, Mistral) or major model APIs (e.g., OpenAI, Anthropic).
 * 5+ years of experience with cloud platforms (Azure preferred) and familiarity with deploying AI models as scalable microservices using Docker and Kubernetes (KFP, KServe).
 * Solid software engineering fundamentals, including version control (Git), automated testing, and CI/CD principles.
 * Excellent communication skills with the ability to articulate complex technical ideas to both technical and non-technical stakeholders.

## Description

* Collaborate with scientists to assess, fine-tune, and deploy LLMs on domain specific data to support accuracy measurement for use cases
 * Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks, guardrail and assessment mechanisms for end to end lifecycle for customized models.
 * Collaborate with product and software engineers to integrate the features into our platform.
 * Establish prompt engineering and data management best practices for transparency and governance.
 * Establish best practices for monitoring and evaluation of data and models across the model lifecycle (development, testing, and production) Keep a pulse on the latest advancements in NLP, LLMs, and agentic AI research, and act as a subject matter expert on architecture decisions on platform and use cases.


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