We are looking for a forward-thinking
AI & Automation Engineer
to lead the transformation of how our business operates, replacing legacy manual processes with intelligent, AI-driven workflows that scale. This is not a support role; it is a
strategic engineering position
with direct impact on operational efficiency, decision-making speed, and competitive advantage.
You will own the full lifecycle of automation and AI initiatives: identifying opportunities across business units, designing robust architectures, building and deploying solutions, and measuring their impact. You'll work at the intersection of AI engineering, systems integration, and process transformation, bridging the gap between raw technology and real business value.
Key Responsibilities
AI-First Process Transformation
Lead discovery sessions with business stakeholders to audit manual, repetitive, or error-prone processes and build a
prioritized automation roadmap
.
Design and deploy
AI-powered workflows
using LLMs (e.g., OpenAI, Anthropic, Azure OpenAI) to automate tasks such as document processing, classification, summarization, triage, and decision support.
Build and maintain
AI agents and multi-step pipelines
that operate autonomously across business functions (HR, Finance, Operations, Customer Success, etc.).
Evaluate and adopt emerging AI tooling (e.g., agentic frameworks, RAG pipelines, fine-tuning approaches) where they deliver measurable ROI.
Workflow Automation & Systems Integration
Architect and manage automated workflows using platforms such as
Zapier, Make (Integromat), and Power Automate
, integrating APIs, webhooks, and SaaS platforms.
Connect disparate systems (Monday.com, BambooHR, Google Workspace, Microsoft 365,



CRM/ERP tools) using no-code/low-code and custom API integrations to ensure seamless, real-time data flow.
Develop Python-based automation scripts and microservices where platform-native tooling reaches its limits.
Data & Insights
Build and maintain
Power BI dashboards and automated reporting pipelines
that surface actionable intelligence for both technical and non-technical stakeholders.
Instrument AI and automation workflows with monitoring, logging, and KPI tracking so business impact is continuously measured and visible.
Architecture & Governance
Define standards, reusable patterns, and best practices for AI and automation development across the organization.
Ensure all solutions adhere to
data governance, security, and compliance
requirements.
Maintain thorough documentation of architectures, automations, data models, and AI prompts/configurations.
Collaboration & Enablement
Serve as an internal AI/automation advocate, running enablement sessions, building internal toolkits, and upskilling non-technical teams to self-serve where appropriate.
Partner from IT, with data engineering, and business teams to deliver scalable, production-grade solutions aligned to organizational goals.
Required Qualifications
5+ years
of experience in automation engineering, AI/ML integration, systems integration,



or a closely related discipline.
Demonstrated track record of
replacing manual business processes
with AI or automation solutions with measurable outcomes.
Strong hands-on experience with
LLM APIs
(OpenAI, Anthropic, Azure OpenAI, or similar) including prompt engineering, chaining, tool/function calling, and agent design.
Proficiency in Python/Javascript for scripting, API consumption, data manipulation, and lightweight service development.
Experience building multi-step, production workflows in
Zapier, Make, or Power Automate
, including error handling, branching logic, and webhooks.
Solid
Power BI
skills: DAX, Power Query (M), data modeling, and report distribution.
Strong working knowledge of
REST APIs and JSON
; ability to read API documentation and integrate any SaaS platform.
Experience with
Microsoft 365 or Google Workspace
ecosystems.
Analytical mindset with the ability to translate ambiguous business problems into concrete technical solutions.
English fluency and excellent communication skills
Nice to Have
Experience with
agentic AI frameworks
(e.g., LangChain, LangGraph, AutoGen, CrewAI) or building multi-agent systems.
Familiarity with
Retrieval-Augmented Generation (RAG)
pipelines and vector databases (Pinecone, Weaviate, pgvector).
Exposure to
cloud platforms
(AWS, Azure, GCP) — particularly serverless functions, storage, and managed AI/ML services.
SQL proficiency for data querying and transformation.
Experience deploying or managing
AI models in production
(API-hosted or self-hosted).
Knowledge of
data governanc

📌 Ai & Automation Engineer (España)
🏢 The Parser
📍 España