AI Engineer (LLM / Agentic Systems)

Job Title : AI Engineer

Location : Fremont, CA

Type : Long Term (Onsite Only)

Roles & Responsibilities

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

System Design & Scalability

• 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

Hands-On Engineering

• 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

Cross-Functional Collaboration

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

Must have

AI / ML Expertise

• 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)

Engineering Foundation

• Strong software engineering background (backend systems, APIs, distributed systems)

• Proficiency in Go, Python, or similar languages

• Solid understanding of data structures and algorithms

System Thinking

• Proven ability to design scalable systems (not just prototypes or local solutions)

• Experience handling edge cases, system trade-offs, and production constraints

Preferred Qualifications

• 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

Must have Qualification

• 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

What Success Looks Like

• 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