Design and develop LLM-powered applications using enterprise AI platforms (e.g., AWS Bedrock, Azure OpenAI / Azure AI platforms, Google Vertex AI).
Implement multi-step orchestration workflows that translate user intent into reliable actions and explainable outputs.
Build robust RAG pipelines (vector databases, embeddings, chunking strategies) and validate grounding quality.
Design agent reasoning/control patterns (e.g., planning vs execution separation, tool calling, memory/context management).
Integrate agents with tools/APIs and enterprise workflows with appropriate governance and guardrails.
Create reusable prompt templates/libraries; implement prompt testing frameworks; establish prompt versioning/governance.
Evaluate solutions for quality/safety/latency/cost and iterate quickly.
Partner with platform/LLMOps teammates to deploy, monitor, and improve LLM systems in production.
Build observability and reliability mechanisms for agent-based workflows.
Lead technical discovery, map workflows/pain points, and communicate solutions to technical and executive stakeholders.

Requirements

1–2+ years hands-on GenAI / Agentic AI experience building LLM apps on enterprise platforms (AWS Bedrock / Vertex AI / Azure AI platforms) in a professional setting.
Strong backend engineering experience (Python preferred) delivering production-grade systems.
Hands on professional experience with RAG patterns and implementation.
Ability to communicate clearly and contribute in fast-moving, cross-functional teams.
Computer Science / strong CS fundamentals

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

LLM applicationsbackend engineeringPythonRAG patternsmulti-step orchestration workflowsprompt testing frameworksgrounding quality validationagent reasoning patternstool callingmemory management

Soft Skills

clear communicationcross-functional collaborationtechnical discoverysolution mappingstakeholder communication