#Machine Learning Engineer, Agentic AI

About the Role

• This position is not eligible for Qualcomm immigration sponsorship.
• Seeking an experienced Machine Learning Engineer specializing in Agentic AI to join our Core Enterprise AI team.
• The role focuses on building enterprise-grade AI solutions capable of reasoning, planning, tool usage, workflow orchestration, and autonomous task execution.

Key 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.

Requirements & Tech Stack

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field.
• Strong programming skills in Python.
• Experience with ML frameworks (PyTorch, TensorFlow).
• Experience with popular LLM frameworks (Langchain, LlamaIndex, Transformers).
• Practical experience with LLM deployments and fine-tuning.
• Experience with vector databases and embedding models.
• Strong understanding of RAG architectures and implementation.
• Familiarity with modern AI/ML infrastructure and cloud platforms (AWS, GCP, Azure).
• 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.

Qualifications

• 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.

Compensation

• Pay Scale: $114,500.00 - $171,700.00
• Total compensation includes a competitive annual discretionary bonus program and opportunity for annual RSU grants.
• Highly competitive benefits package.