Application Engineer AI

Allspring Global Investments Holdings, LLC•Charlotte, NC

1d•$120,000 - $130,000•Hybrid

About The Position

Allspring Global Investments is seeking an Application Engineer with AI skills to build and enhance technology platforms supporting Marketing and Distribution, with an emphasis on agentic automation. The engineer will initially support the production agentic RFP/DDQ platform and help develop new agentic capabilities and platforms as additional business use cases emerge. The role is part of the Client Journey Management Technology team and works across the application lifecycle, from development and enhancement through production support and continuous improvement. The position uses a hybrid working model requiring four days per week in the office and is based in Charlotte, North Carolina, or Milwaukee, Wisconsin. The successful candidate will bring strong Python engineering skills, experience supporting production applications, and hands-on exposure to LLM-based solutions. The role contributes to scalable agentic applications and includes collaboration with engineers, application strategists, product owners, QA analysts, and business stakeholders. The stated base pay range is $120,000–$130,000. Depending on the role, the compensation package may also include participation in an incentive program linked to performance.

Requirements

2–4 years of professional software engineering experience, including hands-on development of production applications using Python.
Experience with or strong understanding of LLM-powered applications, including model APIs, prompt design, LLM output-quality evaluation, and RAG concepts such as embeddings, retrieval, grounding, and citations.
Working knowledge of AWS services such as ECS, SQS, S3, and CloudWatch, and experience with PostgreSQL.
Experience with modern software engineering practices, including Git, pull-request workflows, automated testing, production deployment, and support.
Working knowledge of React and TypeScript, with the ability to contribute across backend and frontend components.
Strong analytical and troubleshooting skills, including interpreting logs, reproducing issues, identifying root causes, and documenting findings clearly.
Strong attention to accuracy, quality, traceability, and controls, particularly in regulated environments.
Strong written and verbal communication skills and ability to work effectively with technical teams and business stakeholders.
Ability to collaborate across engineering, product, QA, and business teams to translate business needs into reliable, scalable technology solutions.
Ability to work independently and collaboratively in a cross-functional environment.
Ability to work at a computer and use standard business software for extended periods during the workday.
Ability to communicate effectively with colleagues, clients, and business partners in person and electronically as needed.
Ability to engage in regular in-person interaction and collaboration with team members and other stakeholders.
Ability to work effectively in a professional office environment with ongoing collaboration, activity, and shifting priorities.
Ability to maintain productivity and focus while balancing multiple tasks and interruptions common in an in-person business setting.
Applicants must be legally authorized to work in the United States without employment visa sponsorship now or in the future.

Nice To Haves

Exposure to Amazon Bedrock.
Experience with agentic AI frameworks such as Claude Agent SDK, LangGraph, LangChain, or similar technologies.
Experience with LLM evaluation, observability, and tracing tools such as Arize Phoenix.
Familiarity with containerization, infrastructure-as-code, and CI/CD technologies such as Docker, Terraform, and Jenkins.
Experience using AI-assisted software development tools, such as Claude Code, within structured engineering and code-review practices.
Hands-on experience with Anthropic models, Claude Agent SDK, Amazon Bedrock, or comparable enterprise AI technologies.
AWS certification, such as Cloud Practitioner, Developer, or AI Practitioner.
Experience developing or supporting technology solutions in financial services or another regulated industry.

Responsibilities

Design, build, enhance, and support technology platforms that enable Marketing and Distribution capabilities, with an increasing focus on AI and agentic automation.
Develop scalable, maintainable solutions across Python services, APIs, integrations, and React/TypeScript applications.
Build and enhance agentic capabilities, initially supporting the RFP/DDQ platform and extending reusable AI patterns to other use cases.
Integrate Client Journey Management platforms with business applications, enterprise services, content repositories, and data sources.
Collaborate with application strategists, product owners, QA analysts, engineers, and business stakeholders to translate business needs into sustainable technology solutions.
Follow established practices for development, code review, testing, deployment, documentation, and production readiness.
Monitor application health and performance in pre-production and production, including CloudWatch alarms and Arize Phoenix traces.
Triage alerts and reported issues, diagnose root causes, implement fixes, and escalate complex issues with clear technical analysis.
Investigate AI answer-quality issues by evaluating source retrieval, content, prompts, configuration, and application code.
Deliver bug fixes, technical improvements, and application enhancements throughout the engineering lifecycle.
Maintain runbooks, technical documentation, and known-issues tracking to improve reliability and supportability.
Participate in the team's production support and escalation model as required.
Manage document ingestion and synchronization for the Bedrock Knowledge Base as underlying content changes.
Validate metadata, provenance, and citations after content ingestion.
Work with business and content owners to identify content-quality or freshness issues that may affect platform responses.
Develop and refine prompts, configurations, retrieval approaches, and other components to improve agentic solution accuracy and effectiveness.
Run evaluations against established datasets before releasing AI-related changes.
Build and maintain automated unit, integration, and end-to-end testing across backend and frontend components.
Maintain application dependencies and container images, identifying potential breaking changes and technical risks.
Support model and platform upgrades through regression testing, impact analysis, and documentation.
Strengthen observability, resiliency, security, maintainability, and production readiness across Client Journey Management applications.
Monitor platform usage and cloud costs and identify unusual trends or optimization opportunities.