Principal AI Engineer (AI)

TL;DR

Principal AI Engineer (AI): Building and optimizing next-generation Agentic AI platforms from 0-1 with an accent on designing, coding, and shipping production-grade AI capabilities for external B2B customers. Focus on architecting scalable inference and orchestration systems, addressing real-world AI issues like hallucinations and drift, and ensuring reliability, controllability, and cost-efficiency.

Location: Hybrid in Santa Clara, United States. Additional locations include Milpitas, Mountain View, East Foothills, Los Altos, and Stanford.

Salary: $171,825 – $286,375

Company

hirify.global empowers nearly 80% of the Forbes Global 100 to accelerate business value, unlocking human and machine potential to drive business growth, innovation, and sustainable success.

What you will do

• Design, build, and evolve agentic AI systems that reason, plan, execute, and adapt in production environments.
• Take AI-driven features from concept to production in a true 0–1 product environment.
• Write and review high-quality production Python code across AI pipelines, inference services, and orchestration layers.
• Implement prompt engineering, tool use, memory, evaluation, and guardrails as first-class engineering concerns.
• Integrate and operate LLMs (commercial and/or open-source) including model selection, fine-tuning, embeddings, and RAG.
• Deploy and operate AI services across cloud platforms (AWS, Azure, GCP), including secure enterprise integrations and customer-specific deployments.

Requirements

• 10+ years of professional software development experience, with significant time shipping B2B products used by external customers.
• Strong software engineering foundation with expert-level Python and experience designing production systems.
• Proven experience building, deploying, and operating AI-powered products in production, not just prototypes or research.
• Hands-on experience with LLMs and GenAI systems in real applications (e.g., agents, copilots, automation, decision systems).
• Deep understanding of agent frameworks, prompt engineering, RAG architectures, model evaluation, and safety/guardrails.
• Experience deploying and operating LLMs using AWS SageMaker, Vertex AI, or direct API integrations (OpenAI, Anthropic).

Nice to have

• Contributions to open-source GenAI tooling or internal frameworks used at scale.
• Experience with Supervised fine-tuning, Parameter-efficient tuning methods (LoRA, QLoRA), and reinforcement learning (RLHF).
• Experience deploying LLMs at scale (Kubernetes, model serving, GPU optimization).

Culture & Benefits

• Culture built around its people, allowing employees to be their true authentic self.
• Commitment to equal opportunity employment regardless of race, age, sex, or other protected characteristics.
• Additional rewards may include a variable plan and country specific benefits.
• Encourages applications from diverse backgrounds and experiences.