AI Native Software Engineer

Join our team as an AI Native Software Engineer, where you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements. This role offers a unique opportunity to work across every industry, every enterprise technology stack, and every level of organizational complexity.

Responsibilities

• Design and build production-grade agentic systems end-to-end, including multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability.
• Integrate and abstract across multiple LLM providers, implementing LLMOps in production, including eval harnesses with real quality metrics, prompt versioning, and observability tooling.
• Embed directly with client engineering teams to design, prototype, and deploy agentic solutions, building reusable patterns, accelerators, and playbooks that scale beyond individual client engagements.

Requirements

• 2 to 5 years of software engineering experience in production environments
• Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment — non-negotiable
• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs
• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering
• LLMOps fundamentals: eval harness design, prompt versioning, and production observability
• Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)
• Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience
• Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure