Senior Engineer
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Overview
In this role you build production-grade agentic AI systems that scale across enterprise environments. You'll work directly with client engineering teams to design, deploy, and govern multi-agent architectures and reusable patterns. The position sits at the core of Accenture's AI practice, offering breadth across industries and direct access to leading engineering ecosystems. You'll tackle real-world reliability, latency, and cost tradeoffs, shaping impactful AI deployments. This is an opportunity to lead the design and delivery of mission-critical AI systems at scale.
Compensaciones / Beneficios vendor fellowship access inside leading AI companies
Forward Deployed Engineer programme
broad industry exposure
career development opportunities
global delivery capability
Responsabilidades Architect and govern production-grade agentic systems at enterprise scale, including multi-agent orchestration, RAG pipelines, policy routing, memory management, and observability
Define RAG pipeline standards: chunking, embeddings, quality benchmarks, and metric-backed decisions
Set multi-LLM integration standards with vendor-agnostic architecture and cost governance across providers
Own LLMOps at programme scale: eval strategy, prompt governance, observability, safety monitoring, and cost controls
Lead client engineering engagements at senior level: architecture sessions, PoC delivery, aligning client leadership and delivery
Shape and publish reusable patterns, accelerators, and engineering standards for scalable engagement ramps
Own measurement framework for agentic system quality: metrics for AI impact and business value to stakeholders
Requisitos principales Software engineering experience in production environments
Hands-on production experience with agentic AI solutions
Experience with agentic orchestration frameworks at production depth (e.g., LangGraph, CrewAI, AutoGen)
Direct production experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) with provider abstraction, token management, latency and cost tradeoffs
RAG pipeline ownership: embeddings, chunking, vector databases, context engineering
LLMOps fundamentals: eval harness design, prompt versioning, production observability
Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, IaC (Terraform or Helm)
Strong Python; Java or equivalent backend language; production debugging and observability
People leadership: managing and developing a team of engineers; performance management
Track record of shipping three production agentic systems in four years preferred
leadership
communication with client stakeholders
architecture facilitation
LangGraph
CrewAI
AutoGen