AI Engineer (Agentic/Applied)

You build production-grade agentic AI systems for enterprise environments. As an AI Engineer (Agentic/Applied), you will design, build, and deploy systems across the full enterprise technology stack, working with client engineering teams and leading technical design sessions.

Key Responsibilities

• Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability
• Define RAG pipeline standards: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable
• Set multi-LLM integration standards: vendor-agnostic architecture, fallback routing, and cost governance
• Own LLMOps at programme scale: eval strategy, prompt governance, observability tooling standards, safety monitoring, and cost controls
• Lead client engineering engagements: facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams
• Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice
• Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present programme-level AI impact in business terms to senior client stakeholders

Basic Qualifications

• 8+ 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
• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code
• 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