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