Software Dev Engineer - AI Agents, Ies Latech

Amazon Amazon · Big Tech · SP, Brazil +1 · Software Development

Software Development Engineer focused on building agentic AI capabilities for operators in emerging markets. The role involves scoping pain points, designing mechanisms, building prototypes, integrating LLMs with internal data, and iterating in production, with a focus on improving operator efficiency by automating repetitive judgment calls.

What you'd actually do

  1. Review agent runs in production, triage operator corrections, and determine which are bugs versus signal that the autonomy boundary needs to move.
  2. Pair with operators on workflows you are scoping. Watch them work, ask why behind every step, and translate implicit rules into mechanisms the agent can execute.
  3. Close the feedback gap by ensuring every operator correction becomes a test, an eval, or a prompt update, not lost noise.
  4. Own the roadmap end to end. Prioritise, demo weekly, and articulate design choices in writing.
  5. Partner with operations leaders in each market to identify which workflows are agent-ready and which still need a human in the loop.

Skills

Required

  • Experience (non-internship) in professional software development
  • Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
  • Experience programming with at least one software programming language

Nice to have

  • Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
  • Bachelor's degree in computer science or equivalent
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution

What the JD emphasized

  • agent capabilities
  • autonomy boundary
  • operator corrections
  • trust score, accuracy, latency, and cost per decision

Other signals

  • agent capabilities
  • integrating large language models
  • operator corrections
  • autonomy boundary
  • trust score, accuracy, latency, and cost per decision