Machine learning Architect

Principal AI/ML Architect & Applied AI Lead

Over 20 years of market experience, Intellias brings together technologists, creators, and innovators across Europe, North and Latin America, and the Middle East. Join our international team and help solve the advanced technology challenges of tomorrow.

Project Overview

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

This role combines deep 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 of:

• Technical leadership
• Hands-on implementation
• AI strategy and innovation
• Cross-functional collaboration
• Mentorship and team development

Requirements

Education & Experience

• Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field
• 10+ years of experience in AI/ML, data science, distributed systems engineering, or related domains
• Proven experience designing and deploying production-grade AI solutions at enterprise scale
• Strong background in both research-driven and industrial AI environments
• Experience leading global or distributed technical teams
• Demonstrated success delivering enterprise AI transformation initiatives

Technical Expertise

AI / Machine Learning

• Large Language Models (LLMs)
• Generative AI systems
• NLP / NLU technologies
• Retrieval-Augmented Generation (RAG)
• Agentic AI workflows and orchestration frameworks
• AI governance and Responsible AI practices
• MLOps frameworks and operational AI systems

Data Engineering & Distributed Systems

• Apache Spark
• Databricks
• Delta Lake
• SQL and NoSQL databases
• Distributed computing architectures
• Streaming and batch data pipelines

Cloud & Infrastructure

• Azure and/or AWS
• Docker and Kubernetes
• CI/CD pipelines
• Infrastructure-as-Code (IaC)
• Cloud-native platform architecture
• Infrastructure optimization and cloud cost management

Programming

• Python
• Scala

Additional Qualifications

• Experience building AI platforms serving multiple teams or business units
• Experience operationalizing AI securely in enterprise environments
• Strong communication and stakeholder management skills
• Ability to translate complex technical concepts into business value

Responsibilities

AI Architecture & Strategy

• Lead the design and implementation of AI/ML solutions across multiple business domains
• Drive enterprise adoption of 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

Data & Platform Engineering

• Architect scalable distributed data-processing systems for large-scale datasets and real-time pipelines
• Design and optimize cloud-native AI platforms using modern data engineering frameworks
• Lead cloud migration and modernization initiatives from on-premises environments to Azure and/or AWS
• Implement efficient data pipelines leveraging Spark, Delta Lake, Databricks, Kubernetes, and containerized environments
• Ensure reliability, scalability, observability, security, and cost-efficiency of AI infrastructure

Generative AI & Conversational Systems

• Design and implement enterprise-grade chatbot and conversational AI platforms
• Lead development of RAG pipelines, agentic workflows, and LLM orchestration systems
• Define governance, evaluation, monitoring, and safety strategies for GenAI systems
• Collaborate with research teams to operationalize LLM-based applications securely and responsibly

Leadership & Collaboration

• Lead cross-functional teams composed of data scientists, ML engineers, software engineers, and business stakeholders
• Mentor engineers and researchers on 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 organization

What We Offer

• Opportunity to shape enterprise AI strategy at global scale
• Work on cutting-edge Generative AI and applied ML initiatives
• International and collaborative engineering environment
• Access to modern cloud-native and AI technologies
• Professional growth, leadership exposure, and innovation-driven culture