Design, build, deploy, and optimize production LLM agents, MCP servers, RAG and memory architectures, and multi-agent orchestration systems. Develop AI solutions using Amazon Bedrock or Microsoft Foundry, establish evaluation and monitoring frameworks, implement guardrails, analyze failures, and collaborate on scalable AI products. Requires strong experience with agent frameworks, production deployments, cloud AI services, Docker, and CI/CD.
Intetics Inc., a leading American technology company specializing in custom software application development, distributed professional teams creation, software product quality assessment, and "all-things-digital" solutions, is looking for an AI Developer (LLM / RAG / Agentic AI) to join its innovative team.
Project Responsibilities
- Design, build, and maintain LLM-based agents in production environments, both customer-facing and internal.
- Build MCP servers on top of existing databases and production systems, and orchestrate multiple internal and external MCP servers under a unified agent framework.
- Design and implement memory, context management, and RAG architectures for production use.
- Establish evaluation frameworks, handle edge cases, and continuously improve agent performance after release.
- Work within architectures that combine databases, APIs, interfaces, agents, and orchestration applications.
- Develop AI solutions using Amazon Bedrock and/or Microsoft Foundry.
- Collaborate with cross-functional teams to deliver scalable and reliable AI-powered products.
Candidate's Portrait
We are looking for a hands-on AI Engineer (Middle+ to Senior level) who has successfully delivered AI agents into production and can clearly explain implementation challenges, failure cases, and optimization approaches. The ideal candidate is passionate about AI, actively explores new tools and models, and has a proven track record building intelligent systems beyond experimentation.
- 1.5-2+ years of hands-on experience with LLMs and AI agents in production environments.
- Strong background in Machine Learning, Python development, or Data Science.
- Self-driven, innovative, and continuously learning emerging AI technologies.
Must-Haves
- 6+ years of overall experience in IT as a Software Engineer, ML Engineer, or AI Engineer.
- Hands-on production experience with Amazon Bedrock and/or Microsoft Foundry (Azure AI Foundry, Azure OpenAI).
- Proven experience building and deploying LLM-based agents to production, including customer-facing conversational AI and internal workflow automation agents.
- Strong experience with at least one agent framework: LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, Strands Agents, or OpenAI Agents SDK.
- Deep MCP expertise, including building MCP servers on top of existing databases and systems, integrating external MCP servers, and orchestrating multiple MCP services under a single agent.
- Strong experience with production-grade RAG, memory, and context engineering, including:
- Embeddings
- Chunking strategies
- Hybrid search
- Reranking
- Vector databases
- Short-term and long-term agent memory
- Practical experience implementing agentic patterns such as:
- ReAct
- Planner-Executor
- Reflection
- Router
- Supervisor
- Multi-Agent Systems
- Human-in-the-Loop
- Experience with LLM evaluation, guardrails, monitoring, and iterative optimization based on production usage and failure analysis.
- Strong understanding of system architecture involving databases, APIs, agents, and orchestration applications.
- Hands-on experience with Docker and CI/CD pipelines.
Nice-to-Have
- Experience in fintech, trading, investing, brokerage, or cryptocurrency-related products.
- Background in regulated industries with strong requirements around compliance, data protection, and AI output auditability.
- Broader AWS or Azure cloud expertise beyond AI services.
- Experience with LLM evaluation and observability platforms such as LangSmith, Langfuse, Ragas, or custom evaluation pipelines.
- Advanced practical expertise with modern agentic AI development tools and rapid prototyping approaches.
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