Agentic AI Systems Architect / Developer

We are looking for an Agentic AI Systems Architect who can design and build production-grade AI systems beyond basic chatbots or simple RAG implementations.

The ideal candidate should understand how to architect systems where AI agents can:

• Plan and reason through multi-step tasks
• Retrieve and validate information
• Execute tool/function calls safely
• Work with memory, state, and user context
• Interact with APIs, databases, and internal systems
• Operate in secure enterprise or government environments

Basic knowledge of Node.js is required for API integration, backend connectivity, and service orchestration.

Technical Requirements

The candidate should have hands-on experience with:

Agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or custom orchestration layers

Agent loops , planning/execution flows, task decomposition, reflection, validation, and human-in-the-loop workflows

Tool calling / function calling , structured outputs, JSON schemas, API execution, and tool safety controls

Stateful AI workflows , including state machines, graph-based orchestration, session state, and workflow persistence

Advanced RAG pipelines , including:

• Chunking strategies
• Embedding model selection
• Hybrid search
• Metadata filtering
• Query rewriting
• Reranking
• Context compression
• Retrieval evaluation
• Hallucination mitigation

Memory architecture , including:

• Short-term memory
• Long-term memory
• User-specific memory
• Vector memory
• Persistent knowledge stores

LLM guardrails , including:

• Prompt injection protection
• Permission-aware retrieval
• Output validation
• Policy checks
• Tool execution safety
• Response verification

LLM observability and evaluation , including:

• Tracing
• Prompt/version management
• Eval datasets
• Regression testing
• Latency analysis
• Cost monitoring
• Failure analysis

Enterprise integrations , including REST APIs, databases, CRMs/ERPs, document stores, webhooks, queues, and workflow engines

Vector databases such as Qdrant, Weaviate, Pinecone, Milvus, Chroma, pgvector, Elasticsearch, or OpenSearch

Secure deployment architectures for private cloud, on-premise, offline, or government-secure environments

Node.js basics , including API development, async workflows, service integration, and connecting AI systems to backend applications