Overview
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 ac
Full job description
Main Responsibilities
- 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.
Qualifications And Requirements
- Bachelor’s or Master’s degree in Computer Science, AI, Engineering, or related field.
- 3+ years of software engineering experience (Python and/or TypeScript).
- 1+ year building LLM-powered or agentic AI systems in production or near-production environments.
- Experience with agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel).
- Hands-on experience with automation/orchestration tools (e.g., n8n, Make) in production settings.
- Strong understanding of LLMs, embeddings, prompt engineering, structured outputs, and tool calling.
- Experience designing REST APIs, microservices, and backend systems.
- Familiarity with vector databases and RAG architectures.
- Strong system design, debugging, and communication skills.
Preferred:
- Experience with MCP, A2A, or advanced agent communication patterns.
- Advanced experience with n8n (custom nodes, self-hosting) or Make (complex scenarios).
- Experience combining LLMs with workflow engines for document processing, reporting, chatbots, or decision support.
- Familiarity with AI evaluation and observability tools (e.g., LangSmith, OpenAI Evals, Weights & Biases).
- Experience with multi-agent systems, planning algorithms, RL, fine-tuning, or RLHF.
- Knowledge of CI/CD pipelines and security best practices.
- Experience in regulated industries (e.g., healthcare, finance, defense).
- Relevant cloud or ML certifications.
- Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code tools.
Requirements & qualifications
- Bachelor’s or Master’s degree in Computer Science, AI, Engineering, or related field.
- 3+ years of software engineering experience (Python and/or TypeScript).
- 1+ year building LLM-powered or agentic AI systems in production or near-production environments.
- Experience with agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel).
- Hands-on experience with automation/orchestration tools (e.g., n8n, Make) in production settings.
- Strong understanding of LLMs, embeddings, prompt engineering, structured outputs, and tool calling.
- Experience designing REST APIs, microservices, and backend systems.
- Familiarity with vector databases and RAG architectures.
- Strong system design, debugging, and communication skills.
Preferred:
- Experience with MCP, A2A, or advanced agent communication patterns.
- Advanced experience with n8n (custom nodes, self-hosting) or Make (complex scenarios).
- Experience combining LLMs with workflow engines for document processing, reporting, chatbots, or decision support.
- Familiarity with AI evaluation and observability tools (e.g., LangSmith, OpenAI Evals, Weights & Biases).
- Experience with multi-agent systems, planning algorithms, RL, fine-tuning, or RLHF.
- Knowledge of CI/CD pipelines and security best practices.
- Experience in regulated industries (e.g., healthcare, finance, defense).
- Relevant cloud or ML certifications.
- Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code tools.
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