Offer summary

Qualifications:

2+ years of professional experience in ML/AI engineering, focusing on LLM fine-tuning and deployment., Strong programming skills in Python and familiarity with MLOps tools like MLflow., Experience with tools such as Langsmith and Langchain for LLM pipeline management., Proficiency in cloud platforms (AWS or Azure) and containerization technologies like Docker..

Key responsabilities:

Collaborate with the team to innovate and develop autonomous AI products.

Utilize prompt engineering and fine-tuning techniques to fulfill product specifications.

Design and implement AI-driven features, including chat interfaces for user interaction.

Integrate AI models with third-party tools for various real-world applications.

Job description

About The Role

At Artisan, we're creating AI Employees, called Artisans, and software which is sleek, easy to use, and replaces the endless stack of point solutions. We're starting with outbound sales and our AI BDR, Ava. Our platform contains every tool needed for outbound sales - B2B data, AI email sequences, deliverability optimization tools and so much more.

We're growing very rapidly (closing $xM in new ARR each month). We recently raised a $15M from top investors, and are looking for superstars to join us on our rocketship growth as we relentlessly work towards building a multi-billion dollar company 🦄

Key Responsibilities

Work with the team to discover new ways to build agentic products so we can create truly autonomous AI employees.
Use prompt engineering and fine-tuning with foundational LLMs to meet product requirements.
Design and develop AI-driven features, including chat-based interfaces that interact with our products on behalf of users.
Build systems for integrating AI models with third-party tools, enabling diverse real-world applications.
Stay updated with advancements in LLMs and machine learning to keep Artisan at the forefront of AI & agentic innovation.

Qualifications

2+ years of professional experience in ML/AI engineering, with expertise in LLM fine-tuning and deployment.
Experience with tools like Langsmith and Langchain for managing and optimizing LLM pipelines.
Strong programming skills, particularly in Python, and experience with MLOps tools like MLflow.
Proficiency with cloud platforms (AWS or Azure) and containerization technologies (e.g., Docker).
Solid understanding of distributed systems and software engineering best practices.
Excellent communication skills and the ability to convey complex technical concepts to diverse audiences.

Benefits