Applied AI Engineer – Automation

About the role

As an Applied AI Engineer (Automation) you will deliver high‑impact AI and automation solutions for clients, owning work from requirements discovery through prototype and production deployment. This remote, full‑time position focuses on building reliable, maintainable systems that integrate large language models into real business workflows via APIs, automation platforms, and backend services.

Key responsibilities

• Design, develop, and deploy tailored AI and automation solutions aligned with client objectives.
• Translate business problems into production‑grade AI workflows using Python, automation tools (n8n, Make, Zapier) and LLM platforms/APIs such as OpenAI, IBM watsonx.ai, and Amazon Bedrock.
• Build agentic workflows with LangChain, LangGraph, and Google ADK, including tool calling and structured outputs.
• Implement Retrieval‑Augmented Generation (RAG) pipelines using vector databases (Pinecone, Elasticsearch, pgvector) and graph databases where appropriate.
• Ship fast prototypes and harden them into scalable systems with testing, reliability, deployment, and monitoring.
• Participate in client discovery sessions, run technical demos, and communicate trade‑offs clearly.
• Provide ongoing support, feature enhancements, bug fixes, monitoring, and prompt/model improvements.

Required profile

• Mid‑to‑senior individual contributor comfortable working independently and within cross‑functional teams.
• Strong communication skills to engage with Solutions Architects, Delivery leads, Product Managers, and client stakeholders.
• Ability to translate complex business requirements into technical specifications and deliver production‑ready solutions.

Required skills

• Python programming.
• Automation platforms such as n8n, Make, Zapier.
• Experience with LLM APIs (OpenAI, IBM watsonx.ai, Amazon Bedrock).
• Proficiency with LangChain, LangGraph, and Google ADK for agentic workflows.
• Knowledge of vector databases and search technologies (Pinecone, Elasticsearch, pgvector) and graph databases.