AI Engineer – Generative & Agentic AI Platform

About Practicus AI

Practicus AI empowers enterprise AI transformation through a secure, governed, and open-source platform unifying Generative AI and Data Intelligence.

We’re a dynamic, fast-growing, and fully remote company where collaboration and creativity thrive. As part of our lean, expert team, your work will directly shape the future of enterprise AI.

Role Overview

We’re seeking talented AI Engineers passionate about building real-world Generative AI and multi-agent systems. You’ll work across model orchestration, retrieval-augmented generation (RAG), and vector intelligence, helping enterprises deploy trusted, cost-efficient, and scalable AI.

What You’ll Do

• Design and implement agentic AI workflows using Python and our orchestration platform.
• Build and optimize RAG pipelines with embeddings, vector databases (Milvus, Qdrant, etc.), and model APIs.
• Develop intelligent data access layers that connect enterprise systems with LLMs and reasoning agents.
• Collaborate with backend, DevOps, and data teams to bring production-grade AI services to life.
• Experiment with open-source models, fine-tuning, and evaluation tools to drive practical innovation.

What We’re Looking For

• Strong Python programming skills (experience with LangChain, LangGraph, or similar a plus).
• Hands-on understanding of LLMs, embeddings, or multi-agent architectures.
• Experience integrating AI with databases, APIs, or cloud infrastructure.
• Passion for turning cutting-edge AI research into practical, reliable enterprise features.
• Clear communicator who thrives in a fast-moving, collaborative, fully-remote environment.
• Familiarity with RAG pipelines, vector databases, and model observability tools.

Nice-to-Have

• Understanding of enterprise-grade deployment (Kubernetes, Docker, Helm).
• Experience optimizing inference cost, latency, or security in production AI systems.

Why Join Us

• Work at the frontier of agentic and generative AI, shaping the systems enterprises actually use.
• Build open-source components that make AI adoption safer and faster.
• Fully remote, flexible environment with a focus on impact over bureaucracy.