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# #Machine Learning Engineer - Generative AI

- **Company:** Qualcomm
- **Location:** San Diego, CA, United States (Remote available)
- **Salary:** $114,500.0 - $171,700.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Software Quality, Computer Programming, Computer Engineering, Python (Programming Language), Open Source Technology, Performance Tuning, Tensorflow, Reinforcement Learning, Google Cloud, Pytorch, Large Language Models, Prompt Engineering, Generative AI, Information Technology, Machine Learning Operations, GPT, Microservices
- **Published:** September 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=772ec1e187a4ff12

## About the Role

* Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field., * Experience with popular LLM frameworks (Langchain, LlamaIndex, Transformers)
 * Knowledge of prompt engineering and chain-of-thought techniques
 * Experience with containerization and microservices architecture
 * Background in Reinforcement Learning
 * Contributions to open-source AI projects
 * Experience with ML ops and model deployment pipelines

Skills and Competencies:

 * Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow)
 * Practical experience with LLM deployments and fine-tuning
 * Experience with vector databases and embedding models
 * Familiarity with modern AI/ML infrastructure and cloud platforms (AWS, GCP, Azure)
 * Strong understanding of RAG architectures and implementation
 * Strong problem-solving and analytical skills
 * Excellent communication and collaboration abilities
 * Experience with agile development methodologies
 * Ability to balance multiple projects and priorities
 * Strong focus on code quality and best practices
 * Understanding of AI ethics and responsible AI development

## Description

The ideal candidate will be responsible for designing, developing, and deploying cutting-edge generative AI solutions, with a focus on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Intelligent agent systems., * Design and implement RAG-based solutions to enhance LLM capabilities with external knowledge sources

 * Develop and optimize LLM fine-tuning strategies for specific use cases and domain adaptation
 * Create robust evaluation frameworks for measuring and improving model performance
 * Build and maintain agentic workflows for autonomous AI systems
 * Collaborate with cross-functional teams to identify opportunities and implement AI solutions