Role: Senior Technology Consultant – GenAI / Agent Platform Engineer
Location: Westlake, Texas and Durham, North Carolina; Hybrid
Job Description:
Role Summary:
We are seeking an experienced GenAI / Agent Platform Engineer to design, build, and operate enterprise-grade platforms for Generative AI and AI Agent solutions. The ideal candidate should have strong hands-on expertise in agent orchestration, LLM/model integration, tool invocation, prompt engineering and safety, policy controls, and AI observability/telemetry.
The role will collaborate with application engineering, AI/ML, platform, security, and governance teams to build scalable and secure capabilities that enable development teams to create and operate production-grade AI agents and GenAI applications.
Day-to-Day Job Duties:
· Design and develop enterprise platforms for GenAI applications and AI agents.
· Build agent orchestration workflows supporting planning, reasoning, tool invocation, and multi-step execution.
· Integrate enterprise applications with LLMs and foundation models through secure APIs.
· Develop reusable agent capabilities, tools, APIs, connectors, and platform services.
· Implement tool/function calling to enable agents to securely interact with enterprise systems and data sources.
· Build and manage integrations using Model Context Protocol (MCP) where applicable.
· Implement prompt management, versioning, templates, and reusable prompt libraries.
· Develop prompt safety and security controls, including input/output filtering and prompt-injection defenses.
· Implement policy controls governing model access, tool permissions, data access, and agent behavior.
· Develop guardrails to support responsible and secure use of Generative AI.
· Implement telemetry, tracing, logging, metrics, and observability across agent and LLM workflows.
· Monitor model/agent performance, latency, token consumption, failures, and operational health.
· Build automated evaluation and testing capabilities for prompts, models, tools, and agent workflows.
· Collaborate with security and governance teams to implement auditability and appropriate AI controls.
· Troubleshoot production AI/agent issues and continuously improve platform reliability and performance.
Basic Qualifications:
· 7+ years of experience in Software Engineering, Platform Engineering, AI/ML Engineering, or cloud-native application development.
· 3+ years of hands-on experience developing Generative AI, LLM-integrated applications, or AI Agent solutions.
· 2+ years of experience with agent orchestration, model integration, tool/function calling, and production LLM APIs.
· 2+ years of experience implementing AI observability, security/guardrails, policy controls, or production AI platform capabilities.
Technical Skills:
· Agentic AI: Agent Orchestration, Tool Calling, Multi-Agent Workflows, Agent Lifecycle
· Agent Frameworks: LangGraph, LangChain, CrewAI, AutoGen, Strands or similar
· Model Integration: AWS Bedrock, Azure OpenAI/AI Foundry, OpenAI APIs, Anthropic APIs
· Agent Integration: Model Context Protocol (MCP), APIs, Enterprise Connectors
· GenAI: Prompt Engineering, RAG, Embedding’s, Vector Search
· AI Safety: Guardrails, Prompt-Injection Defense, Content Filtering, Policy Enforcement
· Observability: Agent/LLM Tracing, Logging, Metrics, Evaluation, Token & Latency Monitoring
· Engineering: Python, Java, TypeScript, or similar programming languages
· Platform: REST APIs, Microservices, Docker, Kubernetes
· Cloud: AWS and/or Microsoft Azure
· DevOps: GitHub, CI/CD, Automated Testing
Nice to Have:
· Experience developing MCP servers or MCP-enabled integrations.
· Experience building production solutions with LangGraph, Strands, LangChain, AutoGen, CrewAI, or Bedrock Agents.
· Experience with AWS Bedrock, Azure AI Foundry, OpenAI, or Anthropic in production environments.
· Experience building enterprise RAG platforms, vector stores, and knowledge retrieval services.
· Knowledge of agent memory, context management, and conversation state management.
· Experience with LLM evaluation and observability platforms such as Langfuse, LangSmith, Braintrust, or Weights & Biases.
· Experience implementing human-in-the-loop approval and escalation workflows.
· Knowledge of Responsible AI, model governance, data privacy, and audit requirements.
· Experience implementing role-based access controls and fine-grained permissions for agents and tools.
· Experience with Kubernetes and cloud-native AI platforms.
· Strong understanding of API security, OAuth/OIDC, IAM, secrets management, and secure engineering.
· Experience building reusable enterprise AI platforms or internal developer platforms.
· Strong software engineering, architecture, troubleshooting, communication, and problem-solving skills
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