The Job

AI Software Engineering Specialist

Technology, Data & Digital · Software & Web Development · Software Engineering · Data Science · DevOps

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In short

As an AI Software Engineering Specialist, you will design, build, and deploy production-grade agentic AI solutions for enterprise clients. This senior-level, full-time role requires hands-on experience with agentic orchestration, RAG pipelines, LLMOps, and cloud-native technologies, including significant Python expertise.

Responsibilities

  • Design and build production-grade agentic solutions end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability.
  • Build and own RAG pipelines: 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, and open-source models — with fallback routing, token, cost, and latency management.
  • Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring.
  • Embed directly with client engineering teams to analyse the SDLC to identify AI/agentic opportunities and use cases, design, prototype, and deploy agentic solutions — workshops, proofs of concept, code-with sessions, and architecture walkthroughs.
  • Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster.
  • Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms.

Requirements

  • 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: 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.

Benefits

  • 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.
  • Consistently recognized as one of the World’s Best Workplaces™.
#AI#Software Engineering#Agentic AI#LLM#Production#RAG#LLMOps#Cloud#Python#Kubernetes
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Company

Accenture

Job Posted

4 weeks ago

Employment Type

Full Time

WorkMode

On Site

Experience Level

Senior

Locations

Newcastle, United Kingdom

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