Senior Applied AI Engineer at Comcast

Compensation

$98,679 – $231,278/yr

Setup: Remote
Location: Illinois · Colorado · +3
Type: Full-time
Level: not_specified

Description

As a Senior Applied AI Engineer at Comcast's DataBee division, you will lead the design, development, and deployment of advanced analytics and data exploitation solutions for a new SaaS security, risk, and compliance platform. This role involves utilizing sophisticated statistical analysis, algorithms, predictive modeling, and pattern recognition to generate actionable insights and automate security solutions. You will also serve as a technical anchor and mentor within a globally distributed engineering team, contributing to efficiency improvements and fostering a high-energy startup culture.

Required Skills

Python
Microservices Architecture
Software as a Service (SaaS)
Large Language Models (LLMs)
Data Science

Additional Information

Preferred Qualifications: Experience developing and implementing microservices containing sophisticated analytical logic.
Demonstrated expertise in working with cybersecurity data sets and applying machine learning techniques to aid in security compliance and/or threat hunting.
Highly preferred if experienced in the application of machine learning to aid in data parsing and normalization.
Experience designing, developing, and deploying applications powered by Large Language Models (LLMs), including conversational AI, chatbots, and AI assistants.
Experience with Retrieval-Augmented Generation (RAG), semantic search, vector databases, embeddings, and prompt engineering.
Experience integrating foundation models (e.g., OpenAI, Anthropic, Gemini, or open-source LLMs) into production applications through APIs and orchestration frameworks.
Experience evaluating, fine-tuning, and optimizing LLM-based solutions, including model selection, prompt optimization, guardrails, and performance evaluation.
Understanding of AI governance, responsible AI practices, model monitoring, and security considerations for generative AI applications.
Experience building scalable AI/ML pipelines and deploying models using MLOps best practices.
Experience working in Agile Scrum development environments.
Experience working with GitHub and Jira or similar technologies.
Experience using Python unit test frameworks.
Experience using Docker and containerized application deployment.
Experience developing on a SaaS product.
Experience with distributed data processing technologies (e.g., Apache Spark, Apache Flink) is highly desirable.