Senior LLM Engineer – Agentic AI Platform

The role is for a Senior LLM Engineer contributing to the development of an advanced Agentic AI platform tailored for the construction domain. The platform focuses on deterministic, reliable, and production-ready LLM workflows supporting complex decision-making, structured reasoning, and domain-specific automation.

• Strong experience as an LLM Engineer, Applied AI Engineer, or similar role
• Proven expertise in designing deterministic LLM workflows and evaluation pipelines
• Deep understanding of training dynamics, loss functions, and optimization strategies
• Hands-on experience with parameter-efficient fine-tuning methods (LoRA, QLoRA)
• Practical experience implementing structured outputs and validation using Pydantic
• Strong knowledge of tool-calling mechanisms in agent-based AI architectures
• Solid understanding of Model Context Protocol (MCP) architecture and implementation
• Experience building production-grade LLM applications with FastAPI or Flask
• Experience developing UI components using Streamlit
• Strong understanding of REST principles, HTTP methods, WebSockets, and API lifecycle management
• Experience working with relational and non-relational databases (PostgreSQL, MySQL, MongoDB, Redis)
• Experience with vector databases such as Pinecone, Weaviate, or pgvector
• Knowledge of semantic search and retrieval-augmented generation architectures
• Strong problem-solving skills and ability to work in production-oriented AI environments

Nice to have:

• Experience in construction industry solutions or domain-specific AI applications
• Experience with multi-agent orchestration frameworks
• Knowledge of observability and monitoring for LLMs

• Design and implement deterministic workflows ensuring consistent and reliable LLM outputs
• Develop evaluation frameworks to measure and validate deterministic model behavior
• Address core LLM challenges, including improving mathematical reasoning accuracy and output reliability
• Implement guardrails using structured outputs and Pydantic-based input/output validation
• Design and manage tool-calling mechanisms for agent-based architectures
• Build and optimize LLM pipelines using parameter-efficient fine-tuning techniques (LoRA, QLoRA)
• Apply knowledge of training dynamics, loss functions, and optimization strategies
• Implement and maintain Model Context Protocol (MCP) architecture
• Develop and deploy LLM-powered services using FastAPI or Flask
• Build lightweight front-end interfaces using Streamlit for AI-driven workflows
• Design RESTful APIs, manage HTTP methods, and implement WebSocket-based communication
• Oversee API lifecycle management, versioning, and performance optimization
• Integrate structured and semantic data storage solutions (SQL, NoSQL, vector databases)
• Implement semantic search and retrieval pipelines leveraging vector databases