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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer, MarTech - **Company:** McAfee, Inc. - **Location:** San Jose, CA, United States - **Experience:** Expert - **Salary:** $135,910.0 - $223,285.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Microsoft Windows, A/B Testing, Adobe Experience Manager, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Android Software Development, Apple IOS, Apple Mac Systems, Microsoft Azure, Cloud Computing, Cloud Database, Data Infrastructure, Github, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Systems Integration, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Grafana, Software Application Programming, Generative AI, Adobe, AI Platforms, Kubernetes, HuggingFace, Machine Learning Operations, Stable Diffusion, Api Design, Docker, Databricks, Programming Languages, Data Generation - **Published:** July 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e0a8b4ddc2c212a6 ## About the Role * Languages & Frameworks: Expert proficiency in Python. Deep experience with PyTorch or TensorFlow, and LLM orchestration frameworks (LangChain, LlamaIndex). * MarTech Ecosystem: Hands-on experience integrating AI with CDPs (HighTouch), ESPs (Braze, Iterable), or Ad Platforms (Meta Conversions API, Google Enhanced Conversions), Analytics (Adobe CJA), Customer focused websites (Javascript, Adobe Experience Manager) * Data Stack: Mastery of SQL and cloud data warehouses. Experience with Databricks for feature engineering is a huge plus. * Generative AI: Proven experience with Claude fine-tuning models (LoRA, QLoRA) and managing vector databases (Pinecone, Milvus, or Weaviate). * Deployment: Experience with Docker, Kubernetes, and cloud AI services (AWS SageMaker, Google Vertex AI, or Azure AI Studio). * Domain Expertise: Previous experience in a high-growth B2C marketing environment. * Experimentation Mindset: Strong understanding of Bayesian A/B testing and causal inference to measure the true uplift of AI interventions. * Strategic Thinking: Ability to translate vague marketing goals ("we want to increase engagement") into specific technical requirements and model objectives. * Customer TouchPoints: Experience developing applications or integrations with Windows, MacOS, iOS, Android ecosystems. The Stack You'll Work With * Data Foundation: Databricks, HighTouch * AI/ML Engine: Claude, PyTorch, Hugging Face, OpenAI API, LangGraph * Orchestration: Airflow, Prefect, or GitHub Actions * Marketing Execution: Braze * Monitoring: Weights & Biases, Arize, or Grafana * Analytics: Adobe CJA ## Description * Architect Agentic Workflows: Design and deploy AI agents to automate complex marketing tasks such as cross-channel campaign orchestration and real-time lead qualification. * Generative Asset Pipelines: Build and maintain scalable pipelines for automated ad creative generation (text, image, and video) using LLMs and Multimodal models (Stable Diffusion, GPT-4o, Sora) while ensuring brand-safe guardrails. * Real-time Personalization: Implement RAG (Retrieval-Augmented Generation) systems to provide context-aware, personalized content across web, email, and SMS. * Build Predictive Models: Develop and productionalize ML models for high-impact marketing use cases: LTV (Lifetime Value) prediction, churn propensity, and "Next Best Action" engines. * MLOps & Integration: Own the end-to-end lifecycle of models, from feature engineering in SQL/Python to deployment via APIs and monitoring for data drift in production. * Privacy & Ethics: Ensure all AI implementations comply with global privacy standards (GDPR, CCPA) and implement "Privacy-First" AI features like differential privacy or synthetic data generation. * Governance: Implement robust Responsible AI frameworks to ensure that the speed of automation does not compromise the trust of the customer ## Related Videos - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [3x Performance: A Humbling Journey](https://www.wearedevelopers.com/videos/100165-3x-performance-a-humbling-journey) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)