How does swipeandhired work?

You set your target roles, salary floor and locations once. swipeandhired scores matching postings against your profile, drafts tailored application materials with AI, and either lets you swipe to apply, one tap at a time, or runs fully on autopilot. Every application is tracked in your dashboard.

Which ATS platforms does swipeandhired support?

All of them. swipeandhired applies on every applicant tracking system (ATS) employers use, including Workday, SmartRecruiters, Greenhouse, iCIMS, Lever, Ashby, Taleo, SuccessFactors, Oracle Cloud and Jobvite, and on a company's own careers page when it uses none of them. You can also bring any posting yourself by link or pasted text.

Can I cancel my subscription?

Anytime, in one click from the billing portal in Settings. Your plan stays active until the end of the current billing period. No retention calls, no dark patterns.

Is my data used for training AI models?

Our platform AI provider may use the content we send it, your profile and the job posting, to improve its models. We never sell your data. If you bring your own AI provider key, requests go directly to your provider under their terms.

Software Engineer III- AI, LLM & Data Platforms at JPMorgan Chase, Plano, TX

Software Engineer III- AI, LLM & Data Platforms at JPMorgan Chase in Plano, TX. Est. up to $200k. Apply with swipeandhired.

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III at JPMorganChase within the Consumer & Community Banking, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities:

Deliver end-to-end platform features for agent-based artificial intelligence use cases, from technical design through production deployment, monitoring, and ongoing improvements
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Build and maintain reusable services and components that enable teams to create, evaluate, and operate LLM-powered agents at scale
Implement evaluation and testing approaches for LLM systems, including quality measurement, regression testing, and error analysis to improve reliability over time
Develop observability capabilities such as tracing, metrics, logs, and analytics to support healthy operations and fast troubleshooting
Apply security, safety, and governance-by-design patterns, including guardrails, access controls, and audit-ready operational practices aligned to security and governance requirements
Collaborate with product managers and stakeholders to define success metrics, prioritize work, and deliver measurable business impact
Partner across engineering, data, security, and risk and compliance stakeholders to ensure solutions are secure, stable, and scalable
Produce clear documentation and reference implementations that accelerate adoption and promote responsible, consistent engineering practices

Required qualifications, capabilities and skills:

Formal training or certification on software engineering concepts and 3+ years applied experience
Proficiency in Python with strong software engineering fundamentals, including testing practices, version control, and code review
Experience building and operating production services, including incident readiness, performance tuning, and operational stability for data-intensive systems
Hands-on experience delivering machine learning and/or LLM-enabled features into production environments, including monitoring and post-deployment iteration
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Practical experience with prompt engineering and retrieval-augmented generation (RAG), including evaluation methods and quality measurement
Ability to design maintainable systems and make sound technical decisions through ambiguity, balancing delivery speed, risk, and long-term sustainability
Strong communication skills, including the ability to explain technical trade-offs to both technical and non-technical stakeholders
Strong collaboration skills and demonstrated ability to partner effectively across teams to deliver outcomes

Preferred qualifications, capabilities and skills:

Experience with agent orchestration frameworks (for example, LangGraph, LlamaIndex, or comparable orchestration approaches) and LLM evaluation tooling
Experience with continuous integration/continuous delivery (CI/CD) practices and production deployments using Docker and Kubernetes
Familiarity with vector databases, embedding pipelines, and related approaches used in retrieval-augmented generation solutions
Experience with cloud and machine learning platforms such as Amazon Web Services or Databricks (or comparable platforms)
Experience contributing to governance, validation approaches, or guardrail frameworks for enterprise-scale artificial intelligence solutions