AI/ML Applications Engineer

An AI/ML Applications Engineer at Intangible is responsible for analyzing opportunities to boost AI system performance, building NLP pipelines that effectively extract intent and entities, and integrating large language models to facilitate creative workflows. Additionally, the role involves designing and maintaining knowledge graphs and collaborating with product and engineering teams to prototype AI features.

To apply for the AI/ML Applications Engineer role at Intangible, a candidate should possess strong software development skills, knowledge of machine learning principles and evaluation metrics, and proficiency in NLP frameworks such as Transformers or LangChain. Experience with data storage solutions and vector or graph databases is also important.

To succeed as an AI/ML Applications Engineer at Intangible, you should be proficient in Python along with ML libraries like PyTorch or TensorFlow. Familiarity with cloud platforms (AWS, Azure, GCP) and containerization tools like Docker and Kubernetes is highly beneficial, as is experience working with AI systems, recommendation engines, or building AI copilots.

The work culture at Intangible for an AI/ML Applications Engineer is collaborative, remote-first, and creative. The company values open debate and input from all team members, regardless of rank. Teamwork and building trust are crucial, and there's a strong emphasis on shipping innovative solutions regularly to empower creatives in their projects.

As an AI/ML Applications Engineer at Intangible, you can expect a range of benefits, including health insurance coverage for you and your dependents, a 401(k) plan, and flexible paid time off. Intangible's remote-first approach allows for greater work-life balance, catering to team members across North America and Western Europe.

When building NLP pipelines for AI applications, I start by thoroughly analyzing the input data to identify the language patterns and intents. Subsequently, I select the appropriate models for tasks such as entity recognition and sentiment analysis, ensuring they are fine-tuned for the domain. It's also critical to continuously test and iterate on the pipeline to enhance accuracy and efficiency.

My experience with large language models includes both utilization and integration into creative workflows, where I've successfully implemented models like GPT and Claude to assist in content generation and user interactions. I've worked on integrating these models with existing tools, testing them in production environments, and optimizing the output for creative teams.

To keep AI systems performant and personalized, I focus on continuous monitoring and analysis of user interactions. I leverage techniques such as A/B testing and user feedback integration to adjust algorithms and enhance personalization. Utilizing analytics also plays a crucial role in identifying trends among users to improve the system continually.

In a recent project involving machine learning, I worked on developing a recommendation engine for an AI-based storytelling tool. My role included designing the model architecture, selecting appropriate algorithms, and implementing NLP techniques to analyze user preferences. Overcoming challenges such as data sparsity and model bias was key to achieving a high-performance outcome.

Ensuring data integrity and quality in my ML projects involves implementing robust data validation checks and cleaning procedures. I also maintain clear documentation of data sources and transformation processes, allowing for reproducibility. Regular audits and peer reviews help us identify and rectify any inconsistencies before the data is used for model training.

Key competencies for an AI/ML Applications Engineer include strong programming skills, a solid understanding of ML principles, and experience with NLP techniques. Additionally, being proficient in cloud services and containerization is crucial for deploying scalable models, along with the ability to work collaboratively in interdisciplinary teams.

I am quite familiar with graph databases and their unique capabilities in representing complex relationships. I've used databases like Neo4j to model knowledge graphs for AI-driven projects, as they allow for efficient querying and analysis of interconnected data, enhancing the performance of recommendation systems and personalization features.

User feedback plays a crucial role in my development process. It provides valuable insights into how people are interacting with the AI system, allowing me to identify areas for improvement. I often set up feedback loops, where user insights directly inform the next stages of development, ensuring the final product is user-centric and effectively meets their needs.

I keep myself updated with the latest advancements in AI and machine learning by following key publications, participating in relevant online courses, and attending industry conferences and webinars. Engaging in community forums and collaborating with peers also helps me stay informed about emerging technologies and methodologies in the field.

For fine-tuning AI models for domain-specific tasks, I typically start with pre-trained models and then use a carefully curated dataset that reflects the domain's nuances. I implement techniques such as transfer learning and hyperparameter optimization to optimize model performance. It’s also important to evaluate the model against domain-specific metrics.