#Machine Learning Engineer - Generative AI

Qualcomm•San Diego, CA

4d•$114,500 - $171,700

About The Position

We are seeking an experienced Machine Learning Engineer specializing in Agentic AI to join our Core Enterprise AI team. The ideal candidate will design, develop, and deploy next-generation AI systems powered by Large Language Models (LLMs), Agentic AI, Hybrid AI architectures, Retrieval-Augmented Generation (RAG), and autonomous intelligent agents. This role focuses on building enterprise-grade AI solutions capable of reasoning, planning, tool usage, workflow orchestration, and autonomous task execution.

Requirements

Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field.
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

Nice To Haves

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

Responsibilities

Design and implement Agentic AI systems capable of autonomous reasoning, planning, tool usage, and multi-step execution to solve complex enterprise problems.
Build and optimize RAG, Hybrid AI, and knowledge-grounded architectures that augment LLMs with enterprise data, external tools, and domain-specific context.
Develop and advance self-improving AI agents leveraging feedback loops, reflection, reinforcement learning techniques, automated evaluations, memory systems, and continuous learning frameworks.
Research, develop, and apply LLM post-training techniques, including supervised fine-tuning (SFT), preference optimization, reinforcement learning, distillation, and domain adaptation to improve model performance for enterprise use cases.
Create robust evaluation, benchmarking, observability, and safety frameworks for measuring agent quality, reliability, efficiency, and business impact.
Build and maintain multi-agent systems and orchestration frameworks that enable collaborative agent workflows and intelligent task automation.
Collaborate with cross-functional teams to identify high-value opportunities and deliver scalable AI solutions that drive measurable business outcomes.
Stay at the forefront of advancements in Agentic AI, reasoning models, post-training, model routing, autonomous agents, and enterprise AI architectures, bringing innovative solutions into production.