AI Engineer

Compunnel, Inc.; Round Rock (TX); Hybrid; USD 130,000 - 170,000; Full time

Job summary

Compunnel, Inc. seeks an experienced AI Engineer to deliver enterprise-scale AI and autonomous agent solutions from concept to production. You will own the full delivery lifecycle, designing secure, scalable AI architectures and autonomous workflows that automate complex business processes.

The role emphasizes secure-by-design practices, token optimization, and governance, with collaboration across engineering, security, and business leadership to drive adoption and measurable impact.

Qualifications

• Experience deploying AI-based solutions in production with real business impact.
• Hands-on experience building autonomous AI agents and workflows for production processes.
• Strong understanding of modern AI and LLM development practices.
• Experience with agent loops, multi-agent orchestration, sub-agents, and tool use.

Responsibilities

• Design, build, deploy, and operate AI-powered capabilities that automate complex processes.
• Develop autonomous AI agents and agentic workflows for production use cases.
• Translate requirements into scalable, secure AI solution architectures.
• Own end-to-end AI initiatives from design to production adoption.

Skills

AI system design

Context engineering

Token budgeting

Security & compliance

Production AI

API design

Cross-functional collaboration

Stakeholder communication

Education

Bachelor's or Master's in CS/Engineering/AI

Job description

We are seeking an experienced AI Engineer with demonstrated expertise in delivering enterprise-scale AI and autonomous agent solutions from concept through production. The ideal candidate will have hands-on experience designing and building agentic AI systems that automate complex business workflows, while maintaining strong engineering, security, governance, and operational standards. This role will support the transformation of the Security Risk Organization into an AI-enabled organization by identifying high-value AI opportunities, developing autonomous workflows, optimizing token usage and costs, and integrating AI capabilities into existing enterprise processes. The successful candidate will independently translate business and stakeholder requirements into secure, scalable solutions and own the full delivery lifecycle from architecture and implementation through deployment, adoption, and continuous improvement.

Key Responsibilities

• Design, build, deploy, and operate enterprise-scale AI-powered capabilities that automate or improve complex business processes.
• Develop autonomous AI agents and agentic workflows for production business use cases.
• Translate business requirements and stakeholder needs into scalable, secure, and enterprise-aligned AI solution architectures.
• Take end-to-end ownership of AI initiatives, including requirements analysis, solution design, implementation, testing, deployment, production support, and stakeholder engagement.
• Design and implement Spec Driven Development workflows that translate well-formed specifications into secure and verifiable implementations.
• Develop guardrails, policy enforcement mechanisms, and verification processes for AI-generated code and AI-assisted development.
• Build developer-assist and verification capabilities that support automated security checks, design reviews, dependency and software supply-chain analysis, static and dynamic analysis orchestration, and release audit activities.
• Integrate AI solutions with enterprise systems including source control, CI/CD platforms, ticketing systems, security scanning tools, identity platforms, and internal applications through APIs, webhooks, and protocols such as MCP.
• Design agent loops, tool and function calling, multi-agent workflows, and sub-agent orchestration.
• Implement context engineering strategies including agentic retrieval, search, memory architectures, enterprise data grounding, and structured outputs.
• Manage context windows and token budgets to optimize AI solution cost, latency, reliability, and performance.
• Evaluate and improve AI system accuracy, reliability, safety, and business impact through structured testing and evaluation practices.
• Establish secure-by-design and secure-by-default practices across AI applications and agentic systems.
• Apply appropriate software architecture and systems design principles, including well-defined service boundaries, scalable data models, API design, event-driven patterns, observability, and extensibility.
• Integrate AI capabilities with enterprise APIs, cloud services, CI/CD pipelines, security platforms, and business applications.
• Evaluate emerging AI technologies, agentic frameworks, retrieval and memory systems, structured output techniques, evaluation frameworks, and LLMOps practices for potential enterprise adoption.
• Identify opportunities to transition existing automation processes into autonomous agentic AI workflows.
• Optimize AI usage through prompt engineering, context management, caching, token optimization, cost controls, and performance tuning.
• Establish governance and technical standards that support responsible and consistent enterprise AI adoption.
• Collaborate with engineering, cybersecurity, product, architecture, and business leadership teams to evaluate technical trade-offs and drive solution adoption.
• Develop reusable AI engineering patterns, documentation, demonstrations, and technical guidance to accelerate adoption across teams.
• Mentor engineers and contribute to team enablement through knowledge sharing and technical training.
• Monitor production AI solutions and continuously improve reliability, security scalability, and operational performance.
• Ensure AI solutions meet enterprise security, privacy, compliance, and architectural requirements.

Required Qualifications

• Demonstrated experience developing and deploying AI-based solutions in production environments with measurable business or operational impact.
• Hands-on enterprise-scale experience building autonomous AI agents and agentic workflows for production business processes.
• Strong understanding of modern AI and LLM development practices.
• Hands-on experience with agentic system design, including agent loops, multi-agent orchestration, sub-agents, tool use, and function calling.
• Experience with context engineering, including retrieval, search, memory architectures, enterprise data grounding, structured outputs, and context window management.
• Strong understanding of token budgeting and optimization, including cost and latency management for production AI workloads.
• Experience evaluating AI system quality, reliability, safety, and business effectiveness.
• Strong software architecture and systems design skills, including API design, event-driven architecture, data modeling, scalability, and extensibility.
• Experience integrating AI solutions with multiple enterprise systems and platforms using REST APIs, GraphQL, CI/CD pipelines, cloud services, and enterprise tooling.
• Experience designing and implementing secure enterprise applications and AI solutions.
• Understanding of secure development practices and enterprise security and compliance requirements.
• Experience working independently across the complete delivery lifecycle, from requirements and architecture through implementation, deployment, and production adoption.
• Ability to explain complex technical concepts and architecture decisions to both technical and business stakeholders.
• Demonstrated ability to evaluate AI-generated code, identify security and engineering issues, and determine whether generated solutions meet production standards.
• Understanding of AI governance, responsible AI practices, security controls, and enterprise adoption standards.
• Strong analytical, problem-solving, communication, and collaboration skills.

Preferred Qualifications

• Experience applying AI within cybersecurity or security-related domains such as application security, DevSecOps, code analysis, threat modeling, firmware security, or software supply-chain security.
• Experience with secure-by-design and secure-by-default principles.
• Familiarity with OWASP security practices, including the OWASP Top 10 for LLM Applications.
• Familiarity with the NIST Secure Software Development Framework (NIST SSDF).
• Experience with MCP (Model Context Protocol).
• Experience building agent skills, tools, and extensions for AI coding assistants.
• Experience with AI coding and development platforms such as Claude Code, GitHub Copilot, Cursor, or Devin.
• Experience with AI evaluation frameworks, guardrails, prompt and response caching, and LLMOps.
• Experience optimizing production AI systems for token consumption, cost, latency, and scalability.
• Experience with agentic retrieval, vector search, RAG, and enterprise knowledge systems.
• Experience developing AI-powered security automation and software assurance capabilities.
• Experience mentoring engineers or leading technical enablement initiatives.
• Experience defining enterprise AI governance frameworks, standards, and reusable architecture patterns.
• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related technical discipline, or equivalent practical experience.
• Experience delivering AI solutions with organization-wide adoption, high transaction volumes, or broad enterprise impact.
• Experience working with executive stakeholders and leading highly visible enterprise AI initiatives.