Applied AI Engineer - Build Production-Grade AI

Surge Group; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Surge Group in San Francisco is seeking Applied AI Engineers to build production-grade AI products, turning the latest models into reliable workflows used by real customers. You’ll work at the intersection of software, product and machine learning, taking AI features from prototype through production, and shipping quickly to gather direct customer feedback.

Expect to design RAG systems, implement evaluations, and collaborate with small, ambitious teams on 0→1 problems at the frontier of applied

Qualifications

• Strong Python and software engineering fundamentals.
• Hands-on experience building with modern LLMs.
• Experience shipping AI or ML products into production.
• Familiarity with RAG, agents, evaluations or model orchestration.
• Strong product judgment and experimentation skills.
• Ability to move quickly in ambiguous, early-stage environments.
• Genuine interest in building practical AI systems rather than purely academic research.

Responsibilities

• Building production-grade LLM applications and agentic workflows.
• Designing RAG, retrieval and knowledge systems.
• Building evaluations, guardrails and observability for AI products.
• Working with structured and unstructured data at scale.
• Experimenting with models, prompting, fine-tuning and inference strategies.
• Integrating AI systems into real customer workflows.
• Taking AI features from prototype through production.

Skills

Python

LLMs

Production AI

RAG & Agents

Model orchestration

Experimentation

Ambiguity handling

Practical AI systems

Job description

Surge Group in San Francisco is seeking Applied AI Engineers to build production-grade AI products, turning the latest models into reliable workflows used by real customers. You’ll work at the intersection of software, product and machine learning, taking AI features from prototype through production, and shipping quickly to gather direct customer feedback.

Expect to design RAG systems, implement evaluations, and collaborate with small, ambitious teams on 0→1 problems at the frontier of applied