AI Engineer (LLM ​/ Agentic Systems

AI Engineer (LLM / Agentic Systems)

Location:

Fremont, CA

Type:
Long Term

Design and build end-to-end AI/ML pipelines (data ingestion? model? evaluation? deployment)

• Develop and optimize LLM-based solutions and agentic workflows
• Implement model evaluation frameworks (accuracy, performance, reliability, and business impact)
• Ensure high-quality data flow and decision-making logic across the AI lifecycle

• Architect scalable, production-grade AI systems
• Design solutions that go beyond local use cases and handle real-world scale, edge cases, and performance constraints
• Build systems that support orchestration, automation, and decision-making

• Write clean, efficient, and maintainable code (Go preferred or similar backend languages)
• Solve complex problems using strong data structure and algorithm fundamentals
• Participate in live coding and technical problem-solving environments

• Work closely with product, data, and engineering teams to define AI-driven solutions
• Translate business problems into scalable AI systems with measurable impact

• Hands-on experience building AI/ML or LLM-based systems in production environments
• Strong understanding of model evaluation approaches and performance metrics
• Ability to clearly explain AI workflows, model behavior, and decision points
• Experience with end-to-end pipelines (data? model? output)

• Strong software engineering background (backend systems, APIs, distributed systems)
• Proficiency in Go, Python, or similar languages
• Solid understanding of data structures and algorithms

• Proven ability to design scalable systems (not just prototypes or local solutions)
• Experience handling edge cases, system trade-offs, and production constraints

• Experience with agentic AI / autonomous workflows
• Hands-on work with LLM frameworks and orchestration tools
• Experience building systems that make decisions or coordinate tasks autonomously
• Familiarity with cloud platforms (AWS, GCP, or Azure)
• Experience deploying and monitoring AI systems in production

• Engineers who have built real AI systems, not just integrated APIs
• Strong depth in AI/ML concepts combined with practical implementation
• Ability to think critically, explain reasoning, and work through problems live
• Focus on impact, scale, and real-world applications over theoretical knowledge

• Builds scalable AI systems that operate reliably in production
• Demonstrates deep understanding of model behavior and system design
• Contributes to high-impact, real-world AI solutions
• Performs effectively under pressure and can clearly communicate technical thinking