Senior Machine Learning Engineer / Specialist – AI Centre of Excellence

Senior Machine Learning Engineer / Specialist – AI Centre of Excellence

Primary Focus: Machine Learning Engineering, MLOps & Production AI Solutions

Position Overview

We are seeking an experienced Senior Machine Learning Engineer / Specialist to support an enterprise AI Centre of Excellence (AI CoE) and its Machine Learning and Data Science initiatives.

This is a highly technical, hands-on role focused on designing, building, deploying, operationalizing, and supporting end-to-end machine learning solutions within enterprise cloud environments.

The successful candidate will bring expert-level Python and SQL skills, combined with deep hands-on experience with Azure Machine Learning, Databricks, MLflow, CI/CD, MLOps, model deployment, monitoring, and machine learning lifecycle management.

A critical requirement for this position is proven experience taking machine learning models from prototype / proof of concept through production deployment and ongoing operational support. The successful candidate will build scalable ML pipelines and services and integrate machine learning capabilities into enterprise applications and operational workflows.

This role works closely with Data Scientists, Data Engineers, and other AI and technology stakeholders to accelerate the delivery of enterprise AI solutions from initial proof of concept through production.

Key Responsibilities

• Design, develop, deploy, and operationalize end-to-end machine learning solutions in enterprise cloud environments.
• Build production-grade ML solutions covering the complete lifecycle, including data preparation, transformation, feature engineering, model development, experimentation, training, validation, deployment, monitoring, maintenance, and production support.
• Take machine learning models from prototype / proof of concept through scalable production deployment.
• Design and build scalable machine learning pipelines, services, and operational workflows.
• Develop and maintain ML solutions using Python and SQL.
• Build and operationalize machine learning workloads using Azure Machine Learning (Azure ML).
• Develop and support machine learning and data workloads within Databricks.
• Implement and manage MLflow for experiment tracking, model management, deployment, and lifecycle management.
• Design and implement CI/CD pipelines for machine learning solutions.
• Apply MLOps practices to automate model development, testing, deployment, monitoring, versioning, and ongoing lifecycle management.
• Implement automated model deployment and release processes across development and production environments.
• Establish effective model monitoring and production support processes to ensure reliability and ongoing performance.
• Integrate machine learning models and services into enterprise applications, systems, and operational workflows.
• Troubleshoot and resolve production issues involving deployed ML models, pipelines, services, and supporting infrastructure.
• Collaborate closely with Data Scientists, Data Engineers, and technical stakeholders, while being capable of working independently.
• Help establish scalable and repeatable practices that accelerate AI/ML solutions from proof of concept to enterprise production.
• Where applicable, contribute to Generative AI, LLM, Agentic AI, and GenAIOps initiatives.

Required Qualifications

• Senior-level, hands-on experience as a Machine Learning Engineer, ML Specialist, MLOps Engineer, or similar production-focused ML professional.
• Proven experience designing and delivering end-to-end machine learning solutions.
• Demonstrated experience taking ML models from prototype / proof of concept through production deployment.
• Expert-level Python development skills.
• Expert-level SQL skills.
• Deep hands-on experience with Azure Machine Learning (Azure ML).
• Strong hands-on experience with Databricks.
• Strong experience with MLflow, including experiment tracking and model lifecycle management.
• Strong experience implementing MLOps practices and frameworks.
• Experience designing and implementing CI/CD pipelines for machine learning workloads.
• Experience with automated model deployment and release processes.
• Strong experience with model deployment, monitoring, lifecycle management, and ongoing production support.
• Experience with cloud-native machine learning platforms and services.
• Strong understanding of data preparation, transformation, and feature engineering for machine learning.
• Experience building scalable ML pipelines and production services.
• Experience integrating ML models into enterprise applications and operational workflows.
• Demonstrated ability to troubleshoot and support production ML solutions.
• Ability to work independently in a highly technical, hands-on delivery environment.
• Strong collaboration skills and experience working with Data Scientists, Data Engineers, and other technical teams.

Generative AI / LLM – Nice to Have

Experience with one or more of the following would be considered an asset:

• Generative AI (GenAI) solutions and applications.
• Large Language Model (LLM) applications.
• Agentic AI frameworks and solutions.
• GenAIOps practices.
• Deployment and operationalization of GenAI / LLM solutions.
• Evaluation and testing of GenAI solutions.
• Monitoring LLM and GenAI applications in production.
• Lifecycle management of enterprise GenAI solutions.
• Applying production engineering and operational practices to GenAI, LLM, and Agentic AI workloads.

Ideal Candidate Profile

The ideal candidate is not solely focused on model experimentation or traditional Data Science. We are looking for a hands-on Machine Learning Engineer who combines strong ML knowledge with the engineering, MLOps, cloud, automation, and operational expertise required to put machine learning solutions into production and keep them running successfully.

The successful candidate will be comfortable owning the technical lifecycle from data and feature preparation through model development, deployment, monitoring, lifecycle management, and production support, while collaborating closely with Data Science and Engineering teams.

Success in this role requires a strong combination of Machine Learning Engineering, Azure ML, Databricks, MLflow, Python, SQL, MLOps, CI/CD, software engineering, automation, and production deployment experience, along with the ability to independently help move enterprise AI initiatives from proof of concept into scalable, reliable production solutions.