[Hiring] Machine Learning Engineer @Ambush

Role Description

We are seeking a highly skilled and experienced Machine Learning Engineer to design, build, and deploy cutting-edge, AI-enhanced applications. This role is primarily focused on robust backend orchestration, system integration, and leveraging large language models (LLMs) to deliver measurable business value. The ideal candidate has deep expertise in Python backend development and cloud architecture, with a passion for emerging AI frameworks and intelligent workflow design.

Key Responsibilities

• Prototype, develop, and deploy scalable solutions utilizing state-of-the-art frameworks, including LangChain, LangGraph, OpenAI SDK, and Vercel AI SDK, ensuring the application of AI provides clear, measurable value.
• Design and implement the complex backend logic and intelligent workflows required to integrate LLMs, external data sources, and contextual automation into enterprise applications.
• Architect and build high-performance, secure, and scalable AI-enhanced applications and microservices using Python (FastAPI).
• Define, develop, and maintain robust and secure API architectures for seamless internal and external system integration.
• Manage and optimize the deployment, scaling, and operationalization of AI services on major Cloud Platforms (GCP, AWS, or Azure), ensuring best practices for performance and security.
• Implement stringent backend security measures across all services and APIs.

Qualifications

• Expert-level proficiency in Python backend development and building production-grade applications using frameworks like FastAPI.
• Proven experience and deep understanding of LLM application development using LangChain, LangGraph, OpenAI SDK, and/or Vercel AI SDK.
• Strong background in designing and implementing scalable microservices and advanced API architectures.
• Hands-on experience deploying and managing backend services on a major Cloud Platform (GCP, AWS, or Azure).
• Expertise in implementing backend security best practices (e.g., authentication, authorization, data protection).
• A pragmatic approach to integrating AI, focusing on where it delivers tangible, measurable value rather than just technological novelty.