Applied AI Engineer (Automation)

About the Role As an Applied AI Engineer (Automation), you will deliver high-impact AI and automation solutions for clients—from requirements discovery through prototype and production deployment. You'll build reliable, maintainable systems that integrate LLMs into real business workflows via APIs, automation platforms, and backend services. This is a mid-to-senior individual contributor role. What You'll Do

• Design & Deploy: Design, develop, and deploy tailored AI and automation solutions aligned to client objectives.
• Build Workflows & Services: Translate business problems into production-grade AI workflows and services using Python, automation tools (n8n/Make/Zapier or similar), and LLM platforms/APIs (e.g., OpenAI, IBM watsonx.ai, Amazon Bedrock), plus retrieval systems.
• Agentic Systems: Build and deploy agentic workflows using LangChain, LangGraph, and Google ADK, including tool calling and structured outputs.
• Retrieval & Knowledge Systems: Implement RAG pipelines using vector databases and search technologies (e.g., Pinecone, Elasticsearch, pgvector) and graph databases when appropriate.
• Prototype → Production: Ship fast prototypes, then harden them into scalable systems (testing, reliability, deployment, monitoring) independently or with a team.
• Client Partnership: Participate in discovery, run technical calls/demos when needed, and communicate tradeoffs clearly to client and internal stakeholders.
• Ongoing Support & Iteration: Improve deployed solutions through feature work, bug fixes, monitoring, prompt/model improvements, and additional iterations as needed. What We're Looking For
• Mid-to-senior level individual contributor able to own projects end-to-end and drive AI/automation solutions from discovery to production.
• Experience designing and deploying production-grade AI and automation solutions.
• Proficiency in Python, automation tools (n8n/Make/Zapier or similar), and LLM platforms/APIs (OpenAI, IBM watsonx.ai, Amazon Bedrock).
• Experience building agentic workflows with LangChain, LangGraph, and Google ADK, including tool calling and structured outputs.
• Experience implementing Retrieval-Augmented Generation (RAG) pipelines using vector databases and search technologies (e.g., Pinecone, Elasticsearch, pgvector) and graph databases when appropriate.
• Ability to translate business problems into production-grade AI workflows and services, plus strong collaboration with Solutions Architects, Delivery/Engagement leads, and Product Managers for scoping, demos, and tradeoffs.
• Strong communication and client-facing skills with a track record of delivering results. Nice to Have
• Experience with LangChain, LangGraph, and Google ADK.
• Familiarity with vector databases and search technologies (Pinecone, Elasticsearch, pgvector) and graph databases.
• Experience with OpenAI API, IBM watsonx.ai, and Amazon Bedrock.
• Experience with automation/integration tools such as n8n, Make, or Zapier.