AI Engineer

Job Description

AI Engineer | Jobright

The Tone:
This is an early career position at Jobright, located in the United States. Jobright builds a personal AI job search agent that transforms the job search process into an expert-guided journey. This role is crucial for building and scaling business-facing AI agents, managing their entire lifecycle from prototype to production, and directly impacting users’ job search success. Your work will shape how people experience an AI-driven job search, contributing to a seamless and intuitive future.

The TL;DR
• Role: Early Career
• Location: United States

• Mission: Build and scale AI agents from prototype to production to transform the job search process, making it more efficient and effective.
• Tech Stack: Python, FastAPI, Flask, Django, PyTorch, TensorFlow, Git, Docker, Kubernetes, AWS, GCP, Azure, Pinecone, Milvus, Weaviate, SQL, NoSQL

What You’ll Actually Do
• Build: Design, build, and maintain scalable infrastructure to deploy and serve production-grade AI agents, ensuring reliability and performance.
• Optimize: Implement and optimize Large Language Model (LLM) pipelines, focusing on latency reduction, throughput maximization, and efficient resource utilization.
• Automate: Develop automated systems for model monitoring, testing, and continuous integration/continuous delivery (CI/CD) to ensure ongoing reliability, accuracy, and performance.
• Process: Optimize data ingestion and processing layers to support real-time agent responsiveness and complex Retrieval-Augmented Generation (RAG) architectures, enhancing AI interaction quality.
• Architect: Architect and refine APIs and backend services that seamlessly bridge sophisticated AI models with an intuitive, user-facing product experience.

The Must-Haves
• Background: Recent graduate or early-career professional (0–2 years of experience) with a degree in Computer Science, Software Engineering, or a related technical field. Must be authorized to work in and live in the United States.
• Experience: Practical experience with backend frameworks such as FastAPI, Flask, or Django. Familiarity with the deployment of LLMs and a foundational understanding of the infrastructure required to support autonomous agents.
• Skills: Strong proficiency in Python. Practical experience with machine learning frameworks (PyTorch or TensorFlow) and a solid understanding of software engineering best practices including version control with Git, CI/CD principles, and unit testing. Strong foundation in both SQL and NoSQL database management.
• Bonus: Previous internship or project experience in ML Ops, backend engineering, or distributed systems, particularly within an AI-focused company. Hands-on experience with containerization technologies (Docker, Kubernetes) and cloud infrastructure platforms (AWS, GCP, or Azure). Knowledge of vector databases (such as Pinecone, Milvus, or Weaviate).