Artificial Intelligence Engineer

Leon Capital Group (LCG) is seeking an AI Engineer to design, develop, and deploy intelligent AI Agents and Automation solutions for our business and portfolio companies. This is a hands‑on builder role that requires working with teams to understand the workflows, design RAG pipelines, build custom AI Agents, and create intuitive interfaces that bring AI capabilities into existing business systems. The role combines technical AI/ML experience with strong communication skills to identify high‑impact opportunities, design the technical solution, and ensure it is built, deployed, and delivering value.

• This is not a research role.
• This is not a strategy‑only role.
• This is not a role for someone who needs a large team to be effective.

This is a role for someone who can own outcomes end‑to‑end — from requirements through deployment.

• Work with stakeholders to translate business problems into clear, executable AI architectures.
• Conduct discovery sessions to identify high‑value use cases for AI Automation.
• Create proofs‑of‑concept and demos to validate solutions before full implementation.
• Provide ongoing support on AI capabilities, platforms, and systems.

• Design & Build autonomous AI Agents using industry frameworks.
• Develop multi‑Agent systems with specialized roles for complex business processes.
• Implement agent orchestration, task delegation, and collaborative workflows.
• Build memory systems and context management for stateful agent interactions.
• Design & Implement Retrieval‑Augmented Generation (RAG) systems for knowledge‑based applications.
• Build document processing pipelines for ingestion, chunking, and embedding generation.
• Develop and Optimize vector database implementations.
• Implement Semantic Search and Retrieval strategies for accurate context retrieval.
• Create evaluation frameworks to measure RAG system accuracy and relevance.

• Integrate GPT‑4, Claude, Gemini, and other leading LLM APIs into business applications.
• Implement prompt engineering strategies and prompt templates for consistent outputs.
• Build fallback mechanisms and multi‑model routing for reliability and cost optimization.
• Develop response validation, quality control, and error handling systems
• Optimize token usage and implement caching strategies to reduce API costs
• Fine‑tune and customize models when appropriate for specific business use cases.

• Integrate AI solutions with existing systems (CRM, data warehouses, internal tools, marketing platforms).
• Design data flows that support learning, iteration, and continuous improvement.
• Connect agents to data sources, databases, and 3rd party services.
• Implement authentication, rate limiting, and security controls on AI Endpoints.
• Create workflow automation that bridges AI agents with business systems.
• Ensure solutions are explainable, auditable, and aligned with governance standards where applicable.

• 3+ Years focused on AI Deployment Experience.
• 7+ years overall experience in software engineering, data engineering, AI/ML, or applied technical roles.
• Hands‑on compliance framework experience (SOC 2, HIPAA, SEC/FINRA, or state insurance regulations) in a regulated industry.
• Experience with AI agent frameworks & automation platforms (Zapier, n8n, etc.).
• Proven experience building and deploying AI systems in production.
• Knowledge of fine‑tuning and training custom language models.
• Understanding of enterprise systems and CRM platforms (Salesforce, Hub Spot, Go High Level ).
• Knowledge of MLOps practices and model deployment strategies.
• Understanding of embedding models and semantic search algorithms.
• Experience with streaming APIs and real‑time data processing.
• Demonstrated ability to operate independently in high‑ambiguity environments.
• Clear track record of developing real solutions, not just designing them.

• Frontend:
  React, Next.js

• Full Stack Experience in directly building, integrating, and operationalizing AI Systems and Platforms.

• Builder mentality
• Bias toward execution
• Low‑ego collaboration with strong opinions backed by action

• Developing lead qualification and enrichment agents for lead generation.
• Implementing content generation pipelines for marketing automation.
• Building research assistants that synthesize information from multiple sources.
• Creating workflow automation agents that interact with multiple business systems.
• Creating automated document processing and analysis streams.