Senior Software Engineer, IT Software Engineering

Software Engineer In The Ascensus Ai Program

Ascensus is the leading independent technology and service platform powering savings plans across America, providing products and expertise that help nearly 16 million people save for a better today and tomorrow.

As a software engineer in the Ascensus AI program, you will help turn modern LLM capabilities into reliable, observable, production-grade software used by real business teams every day. This is a mid-level software engineering role for someone who is strong in core engineering fundamentals and excited to build practical AI-powered systems.

You will work on:

• AI-powered applications and agents
• Retrieval-augmented generation, or RAG, pipelines
• Agent workflows and orchestration
• Prompt strategies and tool integrations
• Secure integrations with enterprise systems
• Observability, testing, and production reliability
• AI-assisted software development using tools such as Cursor, Claude Code, or similar platforms

We are looking for engineers who:

• Move quickly
• Take ownership
• Learn fast
• Communicate clearly
• Care about quality
• Build secure, maintainable software
• Are excited to work in a fast-moving AI environment

You do not need to be an expert in every part of the AI stack on day one, but you should be curious, technically strong, and motivated to grow.

Required qualifications:

• 3-7 years of professional software engineering experience.
• Bachelor's degree in Computer Science, Computer Information Systems, Business Information Systems, a related technical field, or equivalent practical experience.
• Strong experience with one or more modern programming languages, such as:
• Python
• JavaScript / Type Script
• SQL
• Similar modern development platforms
• Experience building production applications, APIs, integrations, backend services, workflow logic, or similar software components.
• Familiarity with LLM-powered systems, such as:
• RAG
• Chatbots
• Agent workflows
• Tool use
• Prompt engineering
• AI-enabled application development
• Strong software engineering fundamentals, including:
• Clean code
• Source control
• Unit testing
• CI/CD
• Refactoring
• Design patterns
• Maintainability
• Working experience with SQL for data inspection, analysis, troubleshooting, or integration.
• Ability to read application logs, traces, and telemetry to investigate issues and identify root causes.
• Strong problem-solving skills.
• Clear communication skills with technical and non-technical audiences.
• Comfort working in ambiguous, fast-moving environments.
• Curiosity, ownership, and the ability to learn new technologies quickly.

Preferred qualifications:

• Experience with one or more of the following is helpful, but not required:
• Building or contributing to AI/LLM-based systems
• RAG pipelines
• Chatbots or conversational AI products
• Agentic applications
• Prompt strategies
• Tool-calling workflows
• Agent orchestration
• Durable workflows or platforms such as Temporal
• Model Context Protocol, function calling, or enterprise tool integrations
• REST APIs, service-oriented architectures, or event-driven systems
• Azure, Azure AI Foundry, Azure Dev Ops, or related Microsoft cloud tools
• Salesforce or other enterprise platforms
• Observability tools such as Langfuse, New Relic, Open Telemetry, or similar platforms
• AI-powered development tools such as Cursor, Claude Code, Git Hub Copilot, or similar tools
• Technical documentation, diagrams, decision records, or design notes
• Working with quality engineers, SDETs, operations engineers, support teams, and product owners

What success looks like:

• Turn ambiguous business needs into simple, working software.
• Use AI-assisted development tools to move faster without sacrificing quality.
• Build AI systems that are observable, testable, and reliable enough for production use.
• Care about retrieval quality, prompt behavior, tool accuracy, user experience, and measurable business outcomes.
• Learn unfamiliar systems quickly.
• Communicate clearly and ask good questions.
• Make practical technical recommendations.
• Take ownership from idea through design, implementation, deployment, monitoring, and improvement.
• Help the team improve how it builds, tests, operates, and evolves enterprise AI software.

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