AI Native Software Engineer
Teknik, data och digitalt · Mjukvaru- och webbutveckling · Mjukvaruutveckling
I korthet
Accenture is seeking an experienced AI Native Software Engineer in Newcastle to design, build, and deploy production-grade agentic AI solutions. This full-time role involves working with clients to implement complex AI architectures, own RAG pipelines, and manage LLMOps in enterprise environments.
Ansvarsområden
- Design and build production-grade agentic solutions end-to-end, including multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability.
- Build and own RAG pipelines, focusing on embeddings, chunking strategy, vector search, context window engineering, and tuning against real quality targets.
- Integrate and abstract across multiple LLM providers (OpenAI, Anthropic, Vertex AI, open-source models) with fallback routing, token, cost, and latency management.
- Implement LLMOps in production, including eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), and cost and safety monitoring.
- Embed directly with client engineering teams to analyze the SDLC, identify AI/agentic opportunities, and deploy solutions through workshops, proofs of concept, code-with sessions, and architecture walkthroughs.
- Build reusable patterns, accelerators, and playbooks that scale beyond individual client engagements.
- Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness, and present findings to client stakeholders in business terms.
Krav
- Significant software engineering experience in production environments.
- Hands-on experience designing and deploying agentic AI solutions in a production environment – non-negotiable.
- Demonstrated experience with agentic orchestration frameworks like LangGraph, CrewAI, AutoGen, or equivalent at production depth.
- Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code, including provider abstraction, token management, and latency/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.
Förmåner
- Breadth across every industry, every enterprise technology stack, and every level of organizational complexity.
- Vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams.
- Direct pathway to the Forward Deployed Engineer programme.
- Opportunities to keep skills relevant through certifications, learning, and diverse work experiences.
- Support for people’s physical, mental, and financial health.
- Consistently recognized as one of the World’s Best Workplaces™.
#AI#Software Engineering#Agentic AI#LLMOps#RAG