Responsibilities
We are seeking an experienced Full Stack AI Engineer to design, build, and scale enterprise-grade generative AI and agentic AI solutions. This is a senior role for someone who can combine strong software engineering with practical AI expertise, make sound architecture decisions, and take solutions from early prototypes through secure, reliable production deployment.
The successful candidate will work across conversational AI, workflow agents, enterprise knowledge and integration, and self-service agent-building experiences. The role requires hands-on engineering as well as technical leadership, with strong experience in GCP and Microsoft Azure AI ecosystems and the ability to work closely with product, security, data, cloud, and business teams.
Key Responsibilities
- Agentic AI Solutions & Experiences
- Design, build, and deploy production-grade generative AI and agentic AI applications using pro-code frameworks, including conversational assistants, workflow agents, and multi-step or multi-agent solutions.
- Build full-stack agent experiences and reusable agent capabilities, including tools, skills, APIs, workflows, memory, and integration components.
- Enterprise Integration & Architecture
- Integrate AI solutions with enterprise knowledge, data, APIs, and business systems using RAG, search, secure connectors, and appropriate access controls.
- Lead architecture and technology decisions, selecting models, frameworks, and patterns that balance quality, scalability, reliability, security, and cost.
- Production Engineering
- Own deployment, testing, evaluation, monitoring, security, reliability, and continuous improvement of AI applications in production.
- Implement agent security and governance controls covering prompt injection, sensitive data and secrets, identity/RBAC, tool permissions, sandboxing, human approvals, auditability, and policy enforcement.
- Apply strong cloud-native engineering practices across GCP and Microsoft Azure, including APIs, containers, CI/CD, observability, and identity/access management.
- Technical Leadership & Collaboration
- Partner with product, engineering, data, security, cloud, and business teams to translate complex requirements into practical AI solutions.
- Provide technical leadership through design and code reviews, reusable engineering standards, mentoring, and hands-on problem solving.
Qualifications
- Bachelor's or master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 6+ years of professional software, platform, or AI engineering experience, with recent hands-on experience building generative AI or agentic AI applications.
- Strong programming and software engineering skills in Python and/or TypeScript/JavaScript, with experience building APIs, backend services, and modern web applications.
- Strong hands-on experience building pro-code LLM and agentic applications, including agent frameworks, tool/function calling, RAG, workflow orchestration, state and memory management, and enterprise integration.
- Strong hands-on experience with GCP and Microsoft Azure, with deep expertise in at least one platform and practical working experience in the other.
- Experience designing and operating production cloud applications, including containers or managed runtimes, CI/CD, databases, APIs, monitoring, identity, and security.
- Strong system-design and architecture fundamentals, with the ability to balance scalability, reliability, security, maintainability, and cost.
- Strong communication and stakeholder-management skills, with the ability to lead technical discussions and work effectively across engineering and business teams.
Preferred Qualifications
- Experience with modern agent frameworks such as Google ADK, Microsoft Agent Framework, LangGraph, OpenAI Agents SDK, Semantic Kernel, or equivalent.
- Experience with enterprise AI evaluation, observability, governance, AgentOps/LLMOps, or responsible AI practices.
- Experience integrating AI with Microsoft 365/SharePoint, SAP, content/media platforms, or enterprise data and analytics platforms.
- Experience with no-code/low-code agent-building platforms such as Microsoft Copilot Studio, Google agent-building tools, or comparable platforms.
- Experience delivering AI products in large, complex, or regulated enterprise environments; media or content-related experience is an advantage.
