Senior Software Engineer, IT Software Engineering
Indexed description
Section 1: Position Summary
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.
Section 2: Job Functions, Essential Duties and Responsibilities
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
- 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
Section 3: Experience, Skills, Knowledge Requirements
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 / TypeScript
- 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.
- 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 DevOps, or related Microsoft cloud tools
- Salesforce or other enterprise platforms
- Observability tools such as Langfuse, New Relic, OpenTelemetry, or similar platforms
- AI-powered development tools such as Cursor, Claude Code, GitHub Copilot, or similar tools
- Technical documentation, diagrams, decision records, or design notes
- Working with quality engineers, SDETs, operations engineers, support teams, and product owners
- 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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