Sr Engineer, Software - Enterprise Procurement and Planning Engineering Technology

T-Mobile T-Mobile · Telecom · Frisco, TX +4

Senior Software Engineer role focused on enhancing the Enterprise Procurement Platform by integrating AI/ML capabilities, including LLM APIs, RAG pipelines, and AI agent workflows. The role involves designing, implementing, and deploying scalable software solutions with a focus on AI-assisted development practices and responsible AI standards.

What you'd actually do

  1. Evaluate, integrate, and maintain AI/ML capabilities — including LLM APIs, local model deployments, RAG pipelines, and MCP server integrations — to enhance platform intelligence and automation across procurement and supply chain workflows
  2. Experience designing and deploying AI agent workflows — including multi-step tool-use agents, LLM-powered diagnostic agents, and agentic orchestration using LangChain / LangGraph or cloud-native frameworks (AWS Bedrock Agents) — integrated into enterprise microservices
  3. Apply AI-assisted development practices and contribute to responsible AI standards, including prompt engineering, output validation, and guardrails aligned with enterprise security and compliance requirements
  4. Develop software solutions and conduct tests to drive engineering projects and ensure quality deliverables
  5. Contribute to design innovations that improve systems, processes, or services using new frameworks and industry best practices

Skills

Required

  • .NET stack (ASP.NET Core / ASP.NET MVC / ASP.NET Web API / C#)
  • front-end frameworks (e.g., Angular, Razor Pages or Blazor)
  • Oracle SQL / MS SQL
  • Kubernetes
  • API Gateway technologies
  • C#/.NET Core
  • Python
  • SQL
  • Splunk
  • Java-Spring Boot
  • Grafana
  • microservices architecture
  • distributed systems
  • LLM APIs (OpenAI, Azure OpenAI, Anthropic)
  • RAG pipelines
  • vector databases
  • MCP server development
  • .NET or Python backend systems
  • LangChain
  • LangGraph
  • AWS Bedrock Agents
  • tool-use design
  • stateful multi-step reasoning
  • enterprise APIs
  • ERP workflows
  • local model operations (Ollama, vLLM, fine-tuning with LoRA/QLoRA)
  • prompt engineering
  • output validation
  • responsible AI guardrails
  • CI/CD pipelines (GitLab/GitHub)
  • observability
  • operational tooling
  • software engineering
  • platform support
  • enterprise application development
  • API development and integration
  • enterprise SaaS platforms
  • large-scale systems
  • AI/ML integration patterns
  • AI agent design and orchestration
  • AI-assisted development tooling (GitHub Copilot, Cursor, or similar)

Nice to have

  • procurement platforms
  • ERP-adjacent systems
  • SAP Ariba administration

What the JD emphasized

  • AI/ML capabilities
  • LLM APIs
  • RAG pipelines
  • AI agent workflows
  • multi-step tool-use agents
  • agentic orchestration
  • LangChain
  • LangGraph
  • AWS Bedrock Agents
  • responsible AI standards
  • prompt engineering
  • output validation
  • guardrails
  • Kubernetes
  • C#/.NET Core
  • Python
  • SQL
  • Splunk
  • Java-Spring Boot
  • Grafana
  • microservices architecture
  • distributed systems
  • vector databases
  • MCP server development
  • .NET or Python backend systems
  • enterprise APIs
  • ERP workflows
  • local model operations
  • Ollama
  • vLLM
  • fine-tuning with LoRA/QLoRA
  • AI-augmented development practices
  • CI/CD pipelines
  • observability
  • operational tooling

Other signals

  • AI/ML capabilities integration
  • AI agent workflows
  • responsible AI standards