Principal MLOps Architect

Intellias; Portugal; On-site; EUR 120,000 - 180,000; Full time

Job summary

Intellias seeks a highly experienced Principal AI/ML Architect & Applied AI Lead to drive design, development and operationalisation of enterprise-scale AI systems across research and production environments.

The role combines deep ML, Generative AI, distributed data systems, and cloud-native architectures with strategic leadership to deliver end-to-end AI initiatives across global enterprises.

Qualifications

• 10+ years of experience in AI/ML, data science, or distributed systems engineering.
• Production-scale AI solutions experience.
• Strong research and industrial AI background.
• Experience leading global or distributed technical teams.
• Delivery of AI transformation initiatives.
• Experience with Generative AI systems and NLP/NLU.
• Familiarity with cloud platforms and IaC.

Responsibilities

• Lead design and implementation of AI/ML solutions across domains.
• Drive adoption of LLMs, Generative AI, NLP/NLU, and analytics.
• Define AI architecture standards, MLOps, and deployment strategies.
• Evaluate emerging AI tech and identify opportunities.
• Translate research into production-ready AI solutions.
• Architect scalable distributed data-processing systems and real-time pipelines.
• Design cloud-native AI platforms using modern data frameworks.
• Lead cloud migration and modernization to Azure/AWS.
• Implement data pipelines with Spark, Delta Lake, Databricks, Kubernetes.
• Ensure reliability, scalability, observability, and cost-efficiency.
• Design and implement enterprise-grade chatbot/conversational AI platforms.
• Lead development of RAG and LLM orchestration.
• Define governance, evaluation, and monitoring for GenAI.
• Collaborate with research to operationalize LLM-based apps securely.
• Lead cross-functional teams and mentorship.
• Coordinate global AI initiatives across geographies.
• Communicate tech concepts to executives.
• Support innovation programs and AI adoption strategies.

Skills

Leadership

AI/ML Architecture

Distributed Systems

Cloud-Native

MLOps

Python

Scala

Databricks

NLP / NLU

LLMs / Generative AI

Governance / Responsible AI

Team Mentoring

Tools

Databricks

Docker

Kubernetes

Spark

Delta Lake

Job description

We are seeking a highly experienced and hands-on Principal AI/ML Architect & Applied AI Lead to drive the design, development, and operationalisation of enterprise-scale AI systems across research and production environments.

This role combines deep technical expertise in Machine Learning, Generative AI, distributed data systems, and cloud-native architectures with strategic leadership capabilities. The ideal candidate will lead complex AI initiatives end-to-end — from experimentation and research to scalable deployment in global enterprise environments.

The position requires a strong balance between:

• technical leadership
• hands-on implementation
• cross-functional collaboration
• mentoring of engineering and data science teams

Requirements

• 10+ years of experience in AI/ML, data science, or distributed systems engineering.
• Proven experience designing and deploying production-grade AI solutions at enterprise scale.
• Strong background in both research and industrial AI environments.
• Experience leading global or distributed technical teams.
• Demonstrated success in delivering AI transformation initiatives.
• Generative AI systems
• NLP / NLU
• Databricks
• SQL / NoSQL databases
• Distributed computing architectures
• Streaming and batch processing pipelines
• Docker
• Infrastructure-as-Code
• MLOps frameworks
• Python
• Scala
• Experience with AI governance and responsible AI practices
• Experience building AI platforms serving multiple teams or business units
• Experience optimizing cloud infrastructure and reducing operational costs.

Responsibilities

• Lead the design and implementation of AI/ML solutions across multiple business domains.
• Drive enterprise adoption of Large Language Models (LLMs), Generative AI, NLP/NLU, and advanced analytics solutions.
• Define AI architecture standards, MLOps best practices, and scalable deployment strategies.
• Evaluate emerging AI technologies and identify opportunities for innovation and operational impact.
• Translate research initiatives into production-ready AI solutions.
• Architect scalable distributed data-processing systems capable of handling large-scale datasets and real-time pipelines.
• Design and optimise cloud-native AI platforms using modern data engineering frameworks.
• Lead cloud migration and modernisation initiatives from on‑premises environments to Azure and/or AWS.
• Implement efficient data pipelines leveraging Spark, Delta Lake, Databricks, Kubernetes, and containerised environments.
• Ensure reliability, scalability, observability, and cost‑efficiency of AI infrastructure.
• Design and implement enterprise-grade chatbot and conversational AI platforms.
• Lead development of Retrieval‑Augmented Generation (RAG), agentic workflows, and LLM orchestration systems.
• Define governance, evaluation, and monitoring strategies for GenAI systems.
• Collaborate with research teams to operationalize LLM‑based applications securely and responsibly.
• Lead cross‑functional teams composed of data scientists, ML engineers, software engineers, and business stakeholders.
• Mentor engineers and researchers in AI/ML best practices, architecture, and software engineering standards.
• Coordinate global AI initiatives across distributed teams and multiple geographies.
• Communicate technical concepts effectively to executive and non‑technical audiences.
• Support innovation programs and AI adoption strategies across the organisation.