AI Engineer 5 (MLX, Agentic AI, Gen AI platform Services)

Capital One • McLean, VA

7d

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team partners across the company to advance AI science and engineering and build proprietary solutions that create value for customers. The team develops AI models and platforms that help Capital One teams enhance products responsibly and at scale.

The AI Engineer 5 will work with engineers, research scientists, technical program managers, and product managers to build AI-powered products for associates and customers. The role spans foundation model training, large-language-model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It includes using open-source and SaaS AI technologies, developing optimization techniques for production AI systems, and contributing to the technical vision and long-term roadmap for foundational AI systems.

The role also designs and optimizes multi-model orchestration pipelines that combine LLMs, vector search, and domain-specific models. It leads cost-performance governance reviews and technical design councils, and mentors Principal- and Manager-level AI engineers. The position is intended for a deeply technical, adaptable engineer who applies current AI research judiciously, works through ambiguous problems, and builds high-quality systems responsibly.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, and at least 6 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of these fields and at least 4 years of such experience.
• At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.

Nice To Haves

• Experience leading AI systems development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• 7 or more years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
• Experience designing, developing, delivering, and supporting complex AI systems.
• Experience developing AI and ML algorithms or technologies, such as LLM inference, similarity search and vector databases, guardrails, or memory, using Python, C++, C#, Java, CUDA, or Golang.
• Experience developing and applying state-of-the-art techniques to optimize training and inference software for hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Passion for following AI research and systems developments and applying novel techniques judiciously in production.
• Excellent communication and presentation skills, including the ability to explain complex AI concepts to peers.
• Experience architecting and integrating heterogeneous AI systems—including rule-based, retrieval-augmented, and generative components—into unified production pipelines.
• Experience defining and enforcing ethical AI deployment standards, including explainability, fairness, and human-in-the-loop review processes.
• Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression.
• Experience right-sizing models, instance counts, and hardware types for requirements such as context length and token inputs and outputs.

Responsibilities

• Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components, including foundation model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
• Use open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, vector databases, and PyTorch.
• Invent and introduce foundation-model optimization techniques to improve scalability, cost, latency, and throughput in production AI systems.
• Contribute to the technical vision and long-term roadmap for Capital One’s foundational AI systems.
• Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models.
• Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency.
• Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards.
• Mentor Principal- and Manager-level AI engineers, fostering cross-domain learning and organizational technical maturity.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior