Overview:Job Summary: The chatbot era is over-enterprises now want software that acts, not just answers. R2 Technologies is seeking an Agentic AI Engineer to design and ship production AI agents that plan, reason, call tools, and complete multi-step business workflows end to end. You will work across the full lifecycle of an agent-orchestration design, tool integration, memory and context management, evaluation, and production deployment-building systems that power enterprise client applications and our own SmartEnt platform.
Key Responsibilities:- Design and build production AI agents and multi-agent orchestration workflows using LangGraph, LangChain, CrewAI, or the Claude Agent SDK.
- Implement agent reasoning, planning, tool/function calling, and memory and state management under real token, latency, and cost constraints.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models to ground agents in enterprise data.
- Integrate Large Language Models-Anthropic Claude, OpenAI GPT, Google Gemini-into enterprise applications via AWS Bedrock, Azure OpenAI, or direct API and MCP-based tool integration.
- Develop programmatic agent evaluations, including offline and online metrics, trajectory scoring, and failure-mode analysis to move agents from demo to dependable.
- Implement guardrails, tracing, and observability to ensure agent decisions are auditable and compliant with enterprise security and governance standards.
Qualifications:- 3 years of experience in AI/ML Engineering, Backend Engineering, or applied LLM development.
- Strong hands-on proficiency in Python, with working knowledge of Java, TypeScript, or Go.
- Hands-on experience with agentic frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Google ADK, or the Claude Agent SDK.
- Proven experience building RAG architectures with vector databases (Pinecone, Weaviate, ChromaDB, FAISS, or Milvus).
- Strong understanding of prompt engineering, context engineering, embeddings, and model evaluation.
- Experience deploying containerized services on AWS, Azure, or GCP using Docker, Kubernetes, and CI/CD pipelines.
Skills:Agentic AI, LangGraph, LangChain, Python, RAG, MCP, Claude, OpenAI, Vector Databases, AWS Bedrock, Multi-Agent Systems
Skills:Agentic AI,LangGraph,LangChain,Python,RAG,Multi-Agent Systems,LLM Integration,Vector Databases,Prompt Engineering
