AI Engineer. The role, the market signal, and how to build it in your org.
Builds applications and features using AI models, tools and enterprise data. Tests the behavior, integrates the result and works with the operating owners on its use.
In a workforce of people and AI agents, this role takes shape around the workflows it supports. Define its responsibilities and handoffs, then prepare people to perform that work.
Source snapshot 2026-07-17. Postings do not confirm that a vacancy is still open.
564 postings · 339 distinct titles · from 264,613 real job postings · see the source snapshot →
What the postings ask this role to do
9,233 tasks extracted from real AI Engineer job descriptions, classified Automate / Augment / Human-only. In this source classification, 2% of task statements are automation candidates. The share does not measure working time or demonstrated agent performance.
- Run large language model inference.
- Clean and tokenize datasets for model fine-tuning or high-quality context retrieval.
- Maintain code repositories.
- Maintain detailed records of code, testing techniques, and support activities.
- Perform day-to-day operations on vector databases (pinecone, milvus, etc.), including indexing, querying, and optimizing search retrieval.
- Leverage a broad stack of open source and saas ai technologies such as aws ultraclusters, huggingface, vectordbs, nemo guardrails, pytorch, and more.
- Design ai software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
- Develop ai software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
- Test ai software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
- Deploy ai software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
- Contribute to the technical vision and the long term roadmap of foundational ai systems at capital one.
- Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver ai-powered products that change how our associates work and how our customers interact with capital one.
- Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver ai-powered products.
- Invent and introduce state-of-the-art llm optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production ai systems.
- Contribute to the technical vision for foundational ai systems.
From the market's version of this role to your version of it
Start with the work, not the org chart.
Define what this role will build, configure, decide and check. Connect that work to an enterprise-specific project and agreed assessment evidence.