AI Engineer 5 (Gen AI Platform Services - Agentic AI) - Mc Lean ML, AI 🏆

AI Engineer 5 (Gen AI Platform Services - Agentic AI) - Mc Lean 💰 Salary: $229,900 - 286,200 per year At Capital One we are looking for a ML, AI engineer! 🛠️ Our tech stack: Agentic AI, AI, AWS, Azure, C#, Cloud, CUDA, Golang, Hardware, Support, Java, LLM, Machine Learning, Model Training, PyTorch, Python, Scala, Machine-Learning 📝 Rquirements: - We require a bachelors degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, plus at least 6 years of experience developing AI and ML algorithms or technologies, or a masters degree in one of these fields plus at least 4 years of relevant experience - We require at least 6 years of programming experience with Python, Go, Scala, CUDA, or Java - Preferred background includes leading AI system development with tradeoff decisions involving cost, latency, throughput, and accuracy - Preferred experience includes 7 years of deploying scalable, responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or comparable private cloud environments - Preferred experience includes designing, developing, delivering, and supporting complex AI systems - Preferred experience includes developing AI and ML technologies such as LLM inference, similarity search, VectorDBs, guardrails, and memory using Python, C++, C#, Java, CUDA, or Golang - Preferred experience includes applying advanced techniques to optimize training and inference software for better hardware utilization, latency, throughput, and cost - Preferred experience includes building agentic AI systems and agentic workflows - Preferred strength in staying current with the latest AI research and applying new methods thoughtfully in production - Preferred communication and presentation skills with the ability to explain complex AI concepts clearly to peers - Preferred experience architecting and integrating heterogeneous AI systems, including rule-based, retrieval-augmented, and generative components, into unified production pipelines - Preferred experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes - Preferred ability to balance model performance and operational cost through dynamic inference strategies and model compression - Preferred experience right-sizing models, instance counts, and hardware types based on

such as context length, token inputs, and token outputs 👩‍💻👨‍💻