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Role: Forward Deployed AI Engineer Location: Dallas, Orlando and Stratford - On-site
Position Type: Contract
Key Responsibilities:
Develop and deploy Lighthouse AI agents (e.g., Supply Chain Risk, Program Financial Analytics) using the Strands Agents SDK and Amazon Bedrock (Claude).
Write optimized SPARQL queries and build Model Context Protocol (MCP) servers to connect the agents to the existing Amazon Neptune RDF knowledge graph.
Implement multi-agent orchestration patterns (Swarm, Graph) in code, enabling agents to pass context and trigger actions across different Functional Control Towers (FCTs).
Code and configure Agent Standard Operating Procedures (SOPs) and Steering Hooks to ensure deterministic, highly accurate agent behavior in mission-critical scenarios. Implement runtime security guardrails (Agent Control) to enforce ITAR and CUI data segregation, ensuring agents only access and process authorized program data.
Collaborate daily with Client s internal data engineers and domain SMEs to iterate on agent performance and accuracy.
Required Skills & Qualifications:
7 years of experience in software engineering, with at least 2 years focused on AI/ML engineering, LLM application development, or agentic systems.
Mandatory Technical Stack: Deep proficiency in Python, AWS (GovCloud preferred), Amazon Bedrock, and graph database querying (SPARQL/Gremlin for Amazon Neptune). Agent Framework Experience: Hands-on experience building with agent frameworks such as Strands Agents, LangGraph, AutoGen, or CrewAI.
Defense Manufacturing Context: Proven experience working with defense, aerospace, or complex manufacturing data systems (e.g., ERP, PLM, MES, EVMS).
Must understand the structure of a Bill of Materials (BOM) and the basics of defense program finance.
Experience implementing Model Context Protocol (MCP) or similar tool-calling architectures for LLMs.
Role: Forward Deployed AI Engineer Location: Dallas, Orlando and Stratford - On-site
Position Type: Contract
Key Responsibilities:
Develop and deploy Lighthouse AI agents (e.g., Supply Chain Risk, Program Financial Analytics) using the Strands Agents SDK and Amazon Bedrock (Claude).
Write optimized SPARQL queries and build Model Context Protocol (MCP) servers to connect the agents to the existing Amazon Neptune RDF knowledge graph.
Implement multi-agent orchestration patterns (Swarm, Graph) in code, enabling agents to pass context and trigger actions across different Functional Control Towers (FCTs).
Code and configure Agent Standard Operating Procedures (SOPs) and Steering Hooks to ensure deterministic, highly accurate agent behavior in mission-critical scenarios. Implement runtime security guardrails (Agent Control) to enforce ITAR and CUI data segregation, ensuring agents only access and process authorized program data.
Collaborate daily with Client s internal data engineers and domain SMEs to iterate on agent performance and accuracy.
Required Skills & Qualifications:
7 years of experience in software engineering, with at least 2 years focused on AI/ML engineering, LLM application development, or agentic systems.
Mandatory Technical Stack: Deep proficiency in Python, AWS (GovCloud preferred), Amazon Bedrock, and graph database querying (SPARQL/Gremlin for Amazon Neptune). Agent Framework Experience: Hands-on experience building with agent frameworks such as Strands Agents, LangGraph, AutoGen, or CrewAI.
Defense Manufacturing Context: Proven experience working with defense, aerospace, or complex manufacturing data systems (e.g., ERP, PLM, MES, EVMS).
Must understand the structure of a Bill of Materials (BOM) and the basics of defense program finance.
Experience implementing Model Context Protocol (MCP) or similar tool-calling architectures for LLMs.