Machine Learning Engineer - Early Career

Jobright is your personal AI job search agent that transforms the job search process into a fast, expert-guided journey. They are seeking an AI Engineer to build and scale business-facing AI agents, managing the entire lifecycle from prototype to production.

Why Join Us

• Build real, production AI agents used by real users
• High ownership and impact
• Work at the intersection of AI, agents, and product
• Shape how people experience AI-driven job search

Responsibilities

• Design, build, and maintain the scalable infrastructure required to deploy and serve production-grade AI agents
• Implement and optimize Large Language Model (LLM) pipelines, focusing on latency reduction, throughput, and efficient resource utilization
• Develop automated systems for model monitoring, testing, and continuous integration to ensure the reliability of our AI agents
• Optimize data ingestion and processing layers to support real-time agent responsiveness and complex RAG (Retrieval-Augmented Generation) architectures
• Architect and refine APIs and backend services that bridge the gap between AI models and the user-facing product

Qualification

Required

• Recent graduate or early-career professional (0–2 years of experience) with a degree in Computer Science, Software Engineering, or a related technical field
• Strong proficiency in Python and experience with backend frameworks (such as FastAPI, Flask, or Django)
• Practical experience with machine learning frameworks (PyTorch or TensorFlow) and a solid understanding of software engineering best practices (version control, CI/CD, unit testing)
• Familiarity with the deployment of LLMs and an understanding of the infrastructure required to support autonomous agents
• Must live in and be authorized to work in the United States

Preferred

• Previous internship or project experience in ML Ops, backend engineering, or distributed systems within an AI-focused company
• Hands-on experience with containerization (Docker, Kubernetes) and cloud infrastructure (AWS, GCP, or Azure)
• Knowledge of vector databases (such as Pinecone, Milvus, or Weaviate) and their role in production AI systems
• Strong foundation in SQL and NoSQL database management for high-scale data handling