Applied AI Engineer - Agentic Workflows (singapore)

Cohere Cohere · AI Frontier · Singapore · Modeling

Cohere is seeking an Applied AI Engineer to design, build, and deploy production-grade AI agents for enterprise customers. This role involves creating agentic workflows powered by LLMs, integrating them with various tools and data sources, and ensuring they are reliable, observable, safe, and auditable. The engineer will work closely with customers and internal teams to transform business workflows into scalable AI systems, balancing rapid iteration with enterprise-grade requirements.

What you'd actually do

  1. Work with enterprise customers and internal teams to turn business workflows into scalable, production-ready agentic AI systems.
  2. Design and build LLM-powered agents that reason, plan, and act across tools and data sources with enterprise-grade reliability.
  3. Balance rapid iteration with enterprise requirements, evolving prototypes into stable, reusable solutions.
  4. Define and apply evaluation and quality standards to measure success, failures, and regressions.
  5. Debug real-world agent behavior and systematically improve prompts, workflows, tools, and guardrails.

Skills

Required

  • Python
  • JavaScript/TypeScript
  • 3+ years of experience building and shipping production software
  • 2+ years working with LLMs or AI APIs
  • modern LLMs (e.g., GPT, Claude, Gemini)
  • vector databases
  • agent/orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex, or custom solutions)
  • RAG
  • agent workflows
  • evaluation
  • performance optimization
  • prompt engineering
  • tool use
  • multi-step agent workflows (e.g. ReAct)
  • failure handling
  • reason about and balance trade-offs between customization and reuse, as well as autonomy, control, cost, latency, and risk
  • communication skills
  • leading technical discussions with customers or partners

Nice to have

  • fast-moving startup environment
  • delivering AI or automation solutions to enterprise customers
  • human-in-the-loop workflows
  • fine-tuning
  • LLM evaluation techniques
  • cloud deployment and production operations for AI systems
  • applied ML
  • NLP
  • decision systems

What the JD emphasized

  • production-grade AI agents for enterprise customers at scale
  • agentic workflows powered by Large Language Models (LLMs)
  • reliable, observable, safe, and auditable
  • evaluation and quality standards
  • customization and reuse
  • autonomy, control, cost, latency, and risk

Other signals

  • production-grade AI agents for enterprise customers at scale
  • agentic workflows powered by Large Language Models (LLMs)
  • integrate LLMs with tools, APIs, and data sources
  • reliable, observable, safe, and auditable agents