Senior Ai Automation Engineer

Required Skills & Experience 3+ years of professional experience building integrations, automations, or workflow systems Strong proficiency with low‑code/no‑code automation platforms (n8n, AI for business automation‑Tray.ai, Zapier, Make | AI Workflow Automation Software & Tools, or similar) Hands‑on experience with LLM APIs (OpenAI, Anthropic Claude, or similar) and prompt engineering Experience working with REST APIs, webhooks, and authentication patterns (OAuth, API keys, JWT) Understanding of data transformation and formats (JSON, XML, CSV) Ability to read API documentation, debug integration issues, and troubleshoot automation failures Experience building event‑driven workflows and handling asynchronous processes Strong problem‑solving skills and ability to translate business requirements into technical solutions Self‑motivated, disciplined, and comfortable working independently across time zones Great communication skills – ability to explain technical concepts to non‑technical stakeholders Your English is at least at the Upper‑Intermediate level to communicate with our teams Preferred Skills & Abilities Experience building AI agents or multi‑step LLM workflows (LangChain, CrewAI, AutoGen, or custom implementations) Python scripting for data processing, API integration, or ETL workflows Familiarity with workflow orchestration tools or iPaaS platforms (Temporal, Workato, MuleSoft) Understanding of vector databases and semantic search for RAG implementations Experience with freight, logistics, or supply chain systems and terminology Knowledge of SQL and database queries for data analysis Background in operations, RevOps, data engineering, or technical product roles Experience measuring and optimizing automation ROI (cost per call, error rates, processing time) Experience with cloud platforms (AWS, Azure or GCP) and serverless architectures Understanding of microservices architecture and event‑driven systems Responsibilities Build intelligent systems that leverage large language models (LLMs) for decision‑making, data extraction, and process automation Create and maintain integrations between internal systems and third‑party platforms using APIs, webhooks, and event‑driven architectures Develop data pipelines that transform, enrich, and route information across multiple systems and data sources Implement prompt engineering strategies and design multi‑step LLM orchestration workflows Build and optimize automated workflows that eliminate repetitive manual tasks across operations teams Monitor automation health, debug failures, and continuously optimize workflows for reliability, performance, and cost‑efficiency Identify new automation opportunities by collaborating directly with operations, product, and engineering teams Document workflows, create runbooks, and establish best practices for scalable AI automation Participate in code reviews and contribute to shared automation libraries, frameworks, and templates Measure and report on automation performance metrics, including accuracy, latency, cost savings, and operational impact Prototype new AI capabilities and experiment with emerging technologies as part of our Technical Innovation Program Example Projects & Initiatives Build systems that automatically extract structured data from unstructured documents (PDFs, emails, images) Create validation workflows that use AI to verify data accuracy and flag anomalies for human review Design document classification systems that route different document types to appropriate processing pipelines Implement OCR + LLM workflows that handle handwritten or poorly scanned documents AI‑Powered Business Process Automation: Develop AI agents that conduct multi‑step research and qualification processes autonomously Build conversational systems that handle routine inquiries and escalate complex cases appropriately Create intelligent routing and matching systems that optimize business outcomes using historical data and ML models Design automated scheduling and coordination systems that handle complex business logic and constraints System Integration & Data Orchestration: Connect internal platforms with external third‑party services, handling authentication, rate limiting, and error recovery Build real‑time data synchronization pipelines that keep multiple systems consistent Create webhook listeners and event processors that trigger automated workflows based on external events Design data enrichment pipelines that pull information from multiple sources and merge it intelligently Build automated alerting and notification systems that detect anomalies and trigger appropriate responses Create dashboards and reporting workflows that aggregate data from multiple sources Develop predictive systems that use historical patterns to forecast operational needs Design automated quality control systems that continuously validate data accuracy across systems Prototype new AI capabilities using the latest LLM models and techniques Experiment with vector databases, semantic search, and retrieval‑augmented generation (RAG) Build proof‑of‑concepts that demonstrate the business value of emerging AI technologies Contribute to our Technical Innovation Program (TIP) with novel automation approaches Platform & Infrastructure: Develop reusable automation components, templates, and design patterns Create internal tooling that makes it easier for other teams to build and deploy automations Establish monitoring, logging, and observability standards for automation workflows Build testing frameworks that ensure automation reliability and catch regressions #J-18808-Ljbffr