Applied AI Engineer: AI-native Systems, RAG, LLM HybridNYC

FUTURE MODERN; New York (NY); Híbrido; USD 255.000 - 276.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Salary + equity

Health coverage

401(k) match

Flexible time off

Descripción de la vacante

Future Modern is seeking an Applied AI Engineer to design and ship AI-native product intelligence for a venture-backed B2B SaaS client in NYC. You will build systems that connect user actions with code behind them, with a human in the loop.

You will lead end-to-end MLOps, evaluate models to prevent hallucinations, and collaborate with product and design to frame problems before coding. The role blends full-stack work with core AI development.

Formación

• Hands-on experience shipping LLM-powered systems to production.
• Experience with RAG, tool use and agent orchestration.
• Strong model evaluation and hallucination mitigation.
• Understanding of modern model architectures.
• Production MLOps in a live environment.
• Clear communication with engineers, product managers and executives.
• Product judgment: you ask "should we build this?" before "how do we build this?"

Responsabilidades

• Design and build applied AI systems from prototype to production.
• Decide prompting vs fine-tuning and explain tradeoffs.
• Build evaluation frameworks and guardrails to limit hallucinations.
• Own end-to-end MLOps: deployment, monitoring, latency, cost, reliability.
• Contribute full-stack when needed.
• Partner with product and design to frame problems before coding.

Conocimientos

LLM systems

RAG pipelines

Agent orchestration

MLOps production

Model evaluation

Cross-functional comms

Product judgment

Herramientas

LangChain

LlamaIndex

MCP

Custom orchestration

Descripción del empleo

Future Modern is seeking an Applied AI Engineer to design and ship AI-native product intelligence for a venture-backed B2B SaaS client in NYC. You will build systems that connect user actions with code behind them, with a human in the loop.

You will lead end-to-end MLOps, evaluate models to prevent hallucinations, and collaborate with product and design to frame problems before coding. The role blends full-stack work with core AI development.