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Mid-level role, metro location, broad GenAI skillset, and global employer increase applicant competition significantly.
Core LLMOps and vector-search skills transfer across industries, though food domain knowledge adds modest bias.
Explicit years plus required GenAI, cloud, and vector-database expertise make filtering strict.
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Lead design, architecture, build, deployment, and support of complex AI Assistants, AI Agents, and automation solutions using ChatGPT-based copilots, Copilot Studio agents, and Power Automate workflows.
Own and shape AI solutions across Food APAC technology stack including APIs, integration services, vector-search pipelines, LLMOps/AgentOps tooling, and front-end experiences for office users and plant operators.
Translate product requirements into secure, scalable, observable, and cost-efficient services or agents; manage lifecycle from experiment to production; drive reliability, performance, and cost improvements.
Minimum 4 years of relevant work experience, typically 5+ years; includes 3+ years with cloud-native AI/ML or GenAI systems on Azure, AWS, or GCP or 2+ years software development experience.
Strong expertise in Python, cloud-native technologies, APIs, CI/CD, containerization, LLM/RAG frameworks, vector databases, and AI platforms like Copilot Studio, ChatGPT, Power Automate, Salesforce, Service Cloud.
Demonstrated experience leading AI assistant, multi-agent automation, or similar AI workflow development.
Must adhere to enterprise architecture, governance, cybersecurity, and Responsible AI standards.
Experienced in designing scalable Python microservices, APIs, LLM, retrieval augmented generation, and vector search architectures for enterprise-scale AI solutions.
Technical leader capable of end-to-end ownership including monitoring, incident management, and operational excellence in AI application production environments.
Comfortable collaborating with cross-functional and product teams to deliver AI solutions that impact internal teams and customers.