Principal Mlops Architect

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 / No SQL 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 Gen AI 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.