Senior AI Native Software Engineer (Agentic AI)

Senior AI Native Software Engineer (Agentic AI)

Location: Remote with up to 20% travel required (travel will be reimbursed)

Hands-on keyboard SW Engineer role – architecture is built out - Keys

• Able to deploy enterprise software
• Build code, test code, deploy code
• Need to understand difference between autonomous AI vs. Automated AI
• Understand to build agents and Agentic AI
• Python coding
• Need production level code and understands agentic but don't need agentic AI in production
• Agentic code tools can integrate and orchestrate

What You Must Have

• 8–10+ years of software engineering experience
• Strong experience with cloud-native systems (APIs, microservices, containers, serverless)
• Experience building and deploying AI/LLM-based systems in production (agents, RAG, orchestration)
• Proficiency in Python, Java, or similar backend languages
• Experience with:
  • CI/CD pipelines
  • Infrastructure as Code
  • Monitoring and observability tools
  • Hands-on experience with AI platforms (OpenAI, Claude, Vertex AI, or similar)

What We'd Like You to Have

• Experience with agent frameworks (e.g., LangGraph, AutoGen, CrewAI)
• Experience designing multi-agent or distributed AI systems
• Familiarity with enterprise-scale system integration
• Experience optimizing AI workloads for cost and performance

Responsibilities Will Include

• Design and implement AI agents, including:
  • Retrieval (RAG)
  • Orchestration workflows
  • Tool/function invocation
  • Policy-based routing
  • Build evaluation frameworks for accuracy, latency, and reliability
  • Implement observability and monitoring for agent lifecycle
• AI Platform Integration
  • Integrate with AI providers (e.g., OpenAI, Anthropic, Google Vertex, open-source models)
  • Build abstraction layers to support multi-model and multi-provider architectures
  • Optimize model usage for performance, cost, and latency
• Cloud-Native Development
  • Develop scalable services using:
    • Microservices architecture
    • Containers (Docker, Kubernetes)
    • Serverless and event-driven patterns
    • Implement CI/CD pipelines and infrastructure as code (e.g., Terraform, Helm)
    • Ensure production readiness, logging, monitoring, and fault tolerance
• Application Development
  • Build and deploy AI-powered applications aligned to business workflows
  • Integrate AI systems into existing enterprise platforms and APIs
  • Develop backend services and APIs supporting agent workflows
• Testing & Performance
  • Define and execute test strategies for AI systems
  • Measure system performance (latency, throughput, accuracy, cost)
  • Debug and optimize production systems