AI Engineer

Compunnel, Inc.; Round Rock, Northern (TX, KY); Sur place; USD 140 000 - 190 000; Plein temps

Résumé du poste

Compunnel, Inc. seeks an AI Engineer to deliver enterprise-scale AI solutions from concept to production. The role emphasizes autonomous and agentic AI systems, secure-by-design practices, and production engineering across the SDLC.

You will own end-to-end delivery, translate business requirements into scalable AI capabilities, optimize token usage, and collaborate with engineering, security, product, and leadership to measure business impact and reliability.

Qualifications

• Experience with DevSecOps practices.
• Experience with threat modeling and application security.
• Experience designing AI security guardrails and policy enforcement mechanisms.
• Experience with MCP integrations.

Responsabilités

• Design, build, and deploy AI-powered capabilities across the Software Development Lifecycle (SDLC).
• Build and deploy autonomous and agentic AI systems for enterprise-scale production environments.
• Develop Spec Driven Development workflows that translate well-formed specifications into secure and verifiable implementations.
• Implement guardrails, policy enforcement, and verification mechanisms for AI-generated code and AI-assisted development.
• Develop developer-assist and verification capabilities that perform automated security checks, including design reviews, dependency and software supply-chain analysis, static and dynamic analysis orchestration, and release audit support.
• Integrate AI solutions with enterprise systems including source control, CI/CD, ticketing, security scanning, identity platforms, and internal applications using APIs, webhooks, and protocols such as MCP (Model Context Protocol).
• Partner with engineering, security, product, and leadership stakeholders to define requirements, evaluate technical trade-offs, and support solution adoption.
• Apply sound architecture and systems design practices, including service boundaries, data modeling, secure defaults, observability, and extensibility.
• Design and operate agentic systems responsibly and efficiently, including agent loops, sub-agent orchestration, context management, and token budgeting.
• Optimize AI workloads for cost, latency, token consumption, performance, and scalability.
• Evaluate emerging AI technologies including agentic frameworks, tool use, agentic retrieval, memory systems, structured outputs, evaluation frameworks, and LLMOps.
• Establish evaluation and quality practices for AI outputs, measuring accuracy, reliability, safety, and business impact.
• Apply secure-by-design and secure-by-default practices throughout AI solution development and deployment.
• Contribute to team enablement through documentation, demonstrations, reusable patterns, mentoring, and knowledge sharing.
• Demonstrate end-to-end ownership across requirements analysis, architecture, implementation, deployment, adoption, and ongoing improvement.

Connaissances

DevSecOps

Threat Modeling

AI Security

MCP Integrations

LLMOps

Agentic AI

Outils

CI/CD Tools

Security Scanning

Description du poste

Job Summary

We are seeking an AI Engineer with demonstrated experience delivering enterprise-scale AI solutions from concept through production. The ideal candidate will have hands-on experience building and deploying autonomous and agentic AI systems, with strong expertise in Python, artificial intelligence, security, architecture, and production engineering. This role requires end-to-end ownership, strong understanding of token optimization, secure-by-design practices, and the ability to translate business requirements into scalable AI solutions with measurable business outcomes.

Key Responsibilities

• Design, build, and deploy AI-powered capabilities across the Software Development Lifecycle (SDLC).
• Build and deploy autonomous and agentic AI systems for enterprise-scale production environments.
• Develop Spec Driven Development workflows that translate well-formed specifications into secure and verifiable implementations.
• Implement guardrails, policy enforcement, and verification mechanisms for AI-generated code and AI-assisted development.
• Develop developer-assist and verification capabilities that perform automated security checks, including design reviews, dependency and software supply-chain analysis, static and dynamic analysis orchestration, and release audit support.
• Integrate AI solutions with enterprise systems including source control, CI/CD, ticketing, security scanning, identity platforms, and internal applications using APIs, webhooks, and protocols such as MCP (Model Context Protocol).
• Partner with engineering, security, product, and leadership stakeholders to define requirements, evaluate technical trade-offs, and support solution adoption.
• Apply sound architecture and systems design practices, including service boundaries, data modeling, secure defaults, observability, and extensibility.
• Design and operate agentic systems responsibly and efficiently, including agent loops, sub-agent orchestration, context management, and token budgeting.
• Optimize AI workloads for cost, latency, token consumption, performance, and scalability.
• Evaluate emerging AI technologies including agentic frameworks, tool use, agentic retrieval, memory systems, structured outputs, evaluation frameworks, and LLMOps.
• Establish evaluation and quality practices for AI outputs, measuring accuracy, reliability, safety, and business impact.
• Apply secure-by-design and secure-by-default practices throughout AI solution development and deployment.
• Contribute to team enablement through documentation, demonstrations, reusable patterns, mentoring, and knowledge sharing.
• Demonstrate end-to-end ownership across requirements analysis, architecture, implementation, deployment, adoption, and ongoing improvement.

Preferred Qualifications

• Experience with DevSecOps practices.
• Experience with threat modeling and application security.
• Experience designing and implementing AI security guardrails and policy enforcement mechanisms.
• Experience with MCP (Model Context Protocol) integrations.
• Experience with LLMOps and AI evaluation frameworks.
• Experience with AI agent frameworks, agentic retrieval, and memory systems.
• Experience developing reusable AI engineering patterns and enterprise enablement frameworks.