AI Native Software Engineer
Remote • Newcastle
Posted 1mo ago
About the job
You will design, build, and deploy production-grade agentic AI solutions across the full enterprise technology stack. This role involves working directly with client engineering teams, leading technical design sessions, and building reusable patterns and accelerators that scale beyond individual engagements. The position offers broad exposure across industries and enterprise technology stacks, with opportunities for vendor fellowship access and a direct pathway to a Forward Deployed Engineer program.
Responsibilities
- Design and build production-grade agentic solutions, including multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability.
- Build and manage RAG pipelines, focusing on embeddings, chunking strategy, vector search, and context window engineering.
- Integrate and abstract across multiple LLM providers (e.g., OpenAI, Anthropic, Vertex AI, open-source models), managing fallback routing, tokens, costs, and latency.
- Implement LLMOps in production, including eval harnesses with quality metrics, prompt versioning, and observability tooling.
- Embed with client engineering teams to identify AI/agentic opportunities, design, prototype, and deploy solutions.
- Build reusable patterns, accelerators, and playbooks to scale solutions across client engagements.
- Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness, presenting findings to stakeholders.
Requirements
- Significant software engineering experience in production environments.
- Hands-on experience designing and deploying agentic AI solutions in production.
- Demonstrated experience with agentic orchestration frameworks like LangGraph, CrewAI, or AutoGen at a production level.
- Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code, including provider abstraction and token/latency/cost management.
- Experience with RAG pipeline ownership, including embeddings, chunking strategy, vector databases, and context engineering.
- Understanding of LLMOps fundamentals: eval harness design, prompt versioning, and production observability.
- Cloud-native engineering maturity, including Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm).
- Strong Python skills; Java or equivalent backend language is acceptable.
- Production debugging and observability experience.
- Quality of experience is prioritized over years of experience; preference for candidates who have shipped multiple production agentic systems.