AI Engineer

Nablon AI; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Nablon AI is seeking an ambitious AI Engineer to build and deploy production-grade AI systems across LLMs, ML models, APIs, and backend services. You will own end-to-end delivery—from modeling to integration—while optimizing latency, cost, and performance.

Guardrails, monitoring, and robust evaluation are core parts of the role. Collaborate with data scientists, architects, and product teams to translate real-world problems into scalable, reliable solutions and continuously improve system

Qualifications

• AI/ML engineering or applied ML.
• Strong hands-on experience building and shipping AI systems.
• Tech Stack: LangChain, LangGraph, Autogpt, Crewai.

Responsibilities

• Develop production-ready AI applications (LLMs, ML models, APIs).
• Design pipelines: data model inference feedback loops.
• Work on agent-based systems (planning, memory, tool use).
• Build scalable backend services (Python / Golang preferred).
• Optimise latency, cost, and performance of LLM/ML systems.
• Implement evaluation, monitoring, and guardrails.
• Collaborate with data scientists, architects, and product teams.
• Translate real-world problems into deployable systems.
• Integrate AI systems into production workflows and APIs.
• Experiment with new models, frameworks, and tools.
• Continuously improve system performance and reliability.

Skills

AI/ML engineering

Applied ML

Hands-on experience

System deployment

Tools

LangChain

LangGraph

Autogpt

Crewai

Job description

As an AI Engineer, you will build and deploy production-grade AI systems spanning LLMs, ML models, backend systems, and agentic workflows. This is not a research role. This is a builder role with real ownership.

Responsibilities:

• Develop production-ready AI applications (LLMs, ML models, APIs).
• Design pipelines: data model inference feedback loops.
• Work on agent-based systems (planning, memory, tool use).
• Build scalable backend services (Python / Golang preferred).
• Optimise latency, cost, and performance of LLM/ML systems.
• Implement evaluation, monitoring, and guardrails.
• Collaborate with data scientists, architects, and product teams.
• Translate real-world problems into deployable systems.
• Integrate AI systems into production workflows and APIs.
• Experiment with new models, frameworks, and tools.
• Continuously improve system performance and reliability.

Requirements:

• AI/ML engineering or applied ML.
• Strong hands-on experience building and shipping AI systems.
• Tech Stack: LangChain, LangGraph, Autogpt, Crewai.