LLM Engineer

Our client, an international AI development company based in New York, is currently seeking a " LLM Engineer " to lead strategic product development efforts in a fast-paced and collaborative environment.

This role will focus on implementing scalable vector store integrations, building retrieval pipelines, and enabling advanced communication protocols between intelligent agents.

Key Responsibilities

RAG & VectorStore Systems :

Build and maintain end-to-end RAG pipelines for context-augmented generation

Integrate and optimize vector databases (FAISS, Pinecone, Weaviate, Milvus)

Support the engineering backbone of agentic and RAG-based AI systems

Protocol Engineering :

Implement Model Context Protocol (MCP) to maintain stateful LLM interactions

Build Agent-to-Agent (A2A) communication layers for multi-agent orchestration

Enable persistent memory and context sharing across model calls

Platform Enablement :

Collaborate with cross-functional teams to productionize models and workflows

Ensure seamless data flow and model integration across services

Qualifications & Skills

Deep experience with vector databases and RAG architecture

Familiarity with MCP (Model Context Protocol) and A2A (Agent-to-Agent) design patterns

Solid background in Python, cloud-based ML pipelines, and containerization tools

Experience in operationalizing LLM-based systems in production

Detail-oriented with a strong engineering mindset

Effective communicator with technical and non-technical stakeholders

Self-driven and adaptable in a fast-paced R&D environment

Very strong English communication skills, both written and verbal (essential for global collaboration)

Experience with Contract Analysis and automated document understanding

Nice to Have :

Experience with invoice parsing / invoice understanding, including extracting structured data from financial documents

Experience in NLP-to-SQL, natural language querying, or generating structured database queries from unstructured text

Familiarity with legal-tech, document intelligence, or enterprise knowledge extraction systems

Experience with observability, vector hygiene, and evaluation frameworks for RAG / agentic systems

Hands-on work with high-throughput, low-latency AI pipelines