Sr AI Engineer

Thryv

7h•Remote

About The Position

Thryv, Inc. is seeking a Senior AI Engineer to research, design, develop, and implement advanced machine learning (ML) solutions. This role involves ensuring deployed models are scalable, reliable, and provide tangible value. The engineer will develop models for trend prediction, user behavior analysis, and business outcomes, as well as create systems for data categorization like sentiment analysis and document classification. Responsibilities include building and fine-tuning generative models (text, code, media), working with architectures like Transformers and Large Language Models (LLMs), and extracting insights from structured and unstructured data. The role also involves data cleaning, preprocessing, exploratory data analysis, integrating AI solutions with engineering teams, and developing/maintaining a feature store. Successful experimental models will be taken to full production. The engineer will use Infrastructure as Code (IaC) for automated deployment into cloud environments and evaluate/integrate third-party ML services. This includes using services like speech-to-text, integrating NLP and Generative AI for conversational agents, and refining existing third-party ML services. The position requires preparing detailed reports and presentations on ML experiments and providing mentorship to junior engineers.

Requirements

Bachelor’s or foreign equivalent degree in Computer Science, Computer Engineering, or a related field, and 5 years of progressive, post-baccalaureate experience in the job offered or as a Machine Learning Engineer, Data Scientist, Data Engineer, Software Engineer, or in a related/similar position.
5 years of experience in neural networks, reinforcement learning algorithms.
5 years of experience in use of automated pipelines to develop and deploy machine learning (ML) models.
5 years of experience in ML development framework including Tensorflow, PyTorch, or scikit-learn.
5 years of experience in implementing classical ML techniques for classification and prediction, including linear regressions and neural networks.
Alternatively, a Master’s or foreign equivalent degree in Computer Science, Computer Engineering, or a related field and 3 years of experience in the skillsets stated above.
1 year of experience in Large Language Models (LLM), including LLM tuning.
1 year of experience in RAG architecture, including vector databases, different clustering techniques, and reranking.
1 year of experience using Infrastructure as Code (IaC) tools and pipelines to deploy models.

Responsibilities

Research, design, develop, and implement advanced machine learning (ML) solutions.
Ensure deployed models are scalable, reliable, and provide tangible value.
Develop models to predict future trends, user behavior, and business outcomes.
Create systems to categorize data, such as sentiment analysis of customer feedback or document classification.
Build and fine-tune models capable of generating new content, such as text, code, or other media.
Work with architectures like Transformers and Large Language Models, and both structured and unstructured data to extract meaningful insights.
Perform data cleaning, preprocessing, and exploratory data analysis to identify patterns and correlations.
Work closely with various engineering teams to integrate AI solutions into company products and platforms.
Collaborate on the development and maintenance of a robust and performant "feature store".
Take successful experimental models to full production.
Utilize Infrastructure as Code (IaC) tools to automate the deployment of ML models into cloud-based production environments.
Evaluate and integrate third-party ML services and tools into our existing data pipeline and product infrastructure.
Incorporate services like speech-to-text for data aggregation and analysis.
Integrate third-party Natural Language Processing (NLP) and Generative AI (GenAI) capabilities to build sophisticated conversational agents (chatbots).
Refine or maintain the existing third-party ML services we currently interface with, ensuring seamless integration, managing API connections, and optimizing performance.
Prepare detailed reports and presentations on ML experiments, prototypes, and methodologies.
Provide guidance and mentorship to junior engineers on programming best practices and advanced AI/ML techniques.