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Overview The AI Automation Specialist / Agentic Workflow Engineer is a core technical role responsible for converting business challenges in logistics, transportation, warehousing, customer service, and corporate operations into scalable AI-powered workflows and agentic applications. Positioned at the intersection of AI engineering, software development, and operations, this role is directly accountable for designing, building, and maintaining intelligent automation that improves efficiency, accuracy, and decision quality across UNIS's Transportation Management System and broader supply chain ecosystem. The Specialist partners closely with product, operations, IT, and business stakeholders to rapidly prototype and deploy agent-based solutions, ensuring that AI initiatives translate into measurable business impact such as reduced manual work, faster cycle times, better exception handling, and enhanced visibility. This role serves as an internal catalyst for AI adoption, standardizing automation patterns, elevating data-driven decision-making, and helping UNIS maintain a competitive edge through pragmatic, production-ready innovation. Responsibilities 1. Design, develop, and deploy AI agents and agentic workflows that address specific logistics, transportation, warehousing, and customer service use cases, ensuring each solution has a clear business objective, defined success metrics, and measurable impact on operational efficiency and service quality. 2. Build and maintain end-to-end intelligent applications, including frontend interfaces, backend services, databases, APIs, authentication, and deployment pipelines, leveraging modern AI frameworks, local and cloud models, and orchestration tools to deliver stable, secure, and scalable solutions. 3. Collaborate with business stakeholders to identify high-value automation opportunities, translate process pain points into technical requirements, quickly prototype AI-driven workflows, iterate based on feedback, and transition validated prototypes into production-ready systems with appropriate documentation and training. 4. Operate and support AI and automation solutions across cloud, VPS, and local GPU environments, managing deployment, monitoring, performance tuning, and incident troubleshooting to maintain reliability, security, and continuity in mission-critical operations. 5. Continuously evaluate emerging AI technologies, multimodal systems, agent frameworks, and workflow platforms, conducting experiments and technical assessments, and recommending adoption paths that enhance UNIS's AI ecosystem, standardize best practices, and improve the reusability and governance of automation assets. Minimum Requirements 1. Bachelor's degree in Computer Science, Engineering, Data Science, Information Technology, or a related technical field, or demonstrably equivalent hands-on experience enabling independent design and delivery of production-grade software solutions. 2. Minimum of 3 years of experience in software engineering, AI automation, workflow orchestration, systems integration, or closely related technical roles, with a track record of owning solutions from concept through deployment and ongoing support. 3. Strong proficiency in Python and API development/consumption, including experience using modern AI development frameworks and automation platforms to build, integrate, and orchestrate AI-powered components and services. 4. Hands-on experience with AI coding and automation tools such as Ollama, Claude Code, Cursor, Codex, OpenAI SDK, Vercel AI SDK, or comparable agent frameworks and workflow orchestration platforms, including practical use in real or simulated projects. 5. Proven experience building and deploying full-stack applications, covering frontend, backend, databases, authentication, and API integrations, as well as deploying to cloud or VPS environments with an understanding of security, scalability, and maintainability considerations. Preferred Requirements 1. Practical experience with modern web and infrastructure tools such as Next.js, Supabase, Docker, Vercel, Payload CMS, Coolify, or equivalent technologies used to build and host data-driven, AI-enabled applications at scale. 2. Experience designing, implementing, and operating multi-agent systems, ComfyUI workflows, or local GPU-based AI deployments, particularly in contexts involving complex supply chain, logistics, or transportation management scenarios. 3. Prior work in logistics, transportation management, warehousing, or other supply chain domains, with the ability to understand operational processes, data flows, and constraints, and translate them into effective AI automation solutions. 4. Exposure to security, compliance, and governance considerations in AI and automation (e.g., data privacy, access control, safe model usage), with experience implementing appropriate safeguards in production environments. 5. Experience contributing to internal AI standards, libraries, reusable components, or platform-level capabilities that streamline the development and deployment of agentic workflows across multiple business units. Skills 1.

AI & Automation Engineering:

Ability to design agent-based systems, build agentic workflows, integrate local and cloud models, and orchestrate autonomous processes that interface with existing applications, data sources, and operational tools. 2.

Full-Stack Development:

Proficiency in building frontend interfaces, backend services, REST/GraphQL APIs, and databases, coupled with understanding of authentication, authorization, and secure coding practices for production environments. 3.

Programming & Scripting:

Strong hands-on skills in Python and familiarity with additional languages or frameworks commonly used for web and backend development (e.g., JavaScript/TypeScript, Node.js, Next.js), enabling rapid prototyping and iteration. 4.

Systems & DevOps Literacy:

Competence in working with Linux/macOS terminals, SSH, Git, Docker, deployment pipelines, and monitoring tools, supporting reliable operation of AI and automation solutions across cloud, VPS, and local GPU infrastructure. 5.

API & Integration Skills:

Ability to design, consume, and troubleshoot APIs; integrate with third-party services, data platforms, and internal systems; and implement MCP-style and other integration patterns that enable cohesive workflows.