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- Design and build agentic AI systems, including autonomous agents, multi-agent orchestration, workflow state machines, and tool-using agents.
- Develop LLM-driven agents capable of reasoning, planning, retrieval (RAG), and task execution across enterprise systems.
- Build and maintain AI-powered automation workflows using platforms like n8n and Make to orchestrate business processes and cross-application integrations.
- Integrate agents with APIs, CRM/ERP systems, collaboration tools, databases, and payment platforms using tool/function calling, MCP, and A2A patterns.
- Implement robust execution logic (validation, retries, rate limits, fallbacks, error handling) to ensure reliability and scalability.
- Design and manage RAG pipelines using embeddings, vector databases, chunking, and reranking strategies.
- Establish safety guardrails, access controls, and human-in-the-loop workflows for high-risk actions.
- Build evaluation, observability, and tracing pipelines to monitor performance, cost, latency, and reliability.
- Deploy and operate agent services in cloud environments (AWS, Azure, or GCP) using Docker, Kubernetes, Terraform, and CI/CD.
- Monitor production systems, troubleshoot issues, and continuously improve agent performance and policies.
- Prototype and benchmark emerging agentic AI frameworks and models.
- Create technical documentation and communicate AI solutions effectively to cross-functional stakeholders.
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