Senior Applied AI Engineer

NBCUniversal

Senior Applied AI Engineer building analytics, predictive models, and LLM applications for Comcast DataBee’s cybersecurity SaaS platform. Providing technical leadership across distributed engineering teams.

Posted 8/4/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $98,679 - $231,278 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying advanced analytics solutions, leveraging machine learning and data mining techniques to extract insights from complex datasets. Proficient in building scalable AI/ML pipelines and implementing security features within a SaaS environment.

Highest-signal resume keywords

Expertise In PythonMachine Learning Application For Security ComplianceMicroservices Development With Analytical LogicBuilding Scalable AI/ML PipelinesExperience With Docker And Containerized Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PreparationStatistical AnalysisData MiningPredictive ModelingData CleansingMachine LearningStatistical ReportingAPI DevelopmentMLOps Best PracticesAgile Scrum

Soft Skills

Technical DirectionMentorshipCollaboration

Tools & Technologies

GitHubJiraDockerApache SparkApache FlinkOpenAIAnthropicGeminiRAGVector Databases

Industry Keywords

SaaSCybersecurityData ExploitationAI GovernanceGenerative AI Security

Tech Stack

Tools & technologies

ApacheCyber SecurityDockerMicroservicesPythonSpark

About the role

Key responsibilities & impact

Design, develop, and deploy advanced analytics and data exploitation solutions for DataBee’s SaaS security, risk, and compliance platform
Extract knowledge and insights from high-volume, high-dimensional data using data preparation, modeling, analysis, and visualization
Develop software programs, algorithms, and automated processes to cleanse, integrate, and evaluate disparate datasets
Analyze large datasets using statistical analysis, data mining, optimization, machine learning, and statistical techniques
Develop and execute statistical and mathematical solutions to business problems
Create data mining architectures, models, protocols, statistical reporting, and data analysis methodologies
Analyze historical customer behavior and product performance patterns
Develop and deploy predictive models for future customer behavior
Produce analysis deliverables and presentations reporting methodology and results
Develop customer-centric models and optimization tools for streaming and at-rest structured and unstructured data
Understand platform usage and assist with production deployments and customer issue triage
Develop security features and adopt DevSecOps practices
Identify and mitigate production incidents
Build reusable software components, libraries, microservices, and APIs
Apply content management systems, global design patterns, and team coding standards
Provide technical direction, architectural guidance, subject matter expertise, and mentorship within a globally distributed engineering team

Requirements

What you’ll need

7–10 years of relevant work experience
Bachelor's Degree preferred, or some combination of coursework and experience, or extensive related professional experience
Expertise in Python
Experience developing and implementing microservices with sophisticated analytical logic
Expertise working with cybersecurity datasets and applying machine learning for security compliance and/or threat hunting
Experience parsing and cleansing data
Experience applying machine learning to data parsing and normalization highly preferred
Experience designing, developing, and deploying LLM-powered applications, including conversational AI, chatbots, and AI assistants
Experience with RAG, semantic search, vector databases, embeddings, and prompt engineering
Experience integrating foundation models such as OpenAI, Anthropic, Gemini, or open-source LLMs through APIs and orchestration frameworks
Experience evaluating, fine-tuning, and optimizing LLM solutions, including model selection, prompt optimization, guardrails, and performance evaluation
Understanding of AI governance, responsible AI, model monitoring, and generative AI security
Experience building scalable AI/ML pipelines and deploying models using MLOps best practices
Experience in Agile Scrum environments
Experience with GitHub and Jira or similar technologies
Experience with Python unit test frameworks
Experience with Docker and containerized application deployment
Experience developing on a SaaS product
Distributed data processing experience with Apache Spark or Apache Flink highly desirable
Flexible schedule and regular, consistent, punctual attendance

Benefits

Comp & perks

Base pay within the posted salary range
Bonus eligibility for most non-sales positions
Best-in-class benefits
Personalized benefits options and expert guidance
Always-on tools supporting physical, financial, and emotional well-being
Access to a variety of teams, locations, and resources
Inclusive equal opportunity workplace