Description
Role Description
The MLOps Engineer / Model Risk Analyst / Responsible AI Specialist supports the reliable, secure, and responsible development and operation of machine-learning and AI systems.
This role combines machine-learning engineering, model governance, risk assessment, and operational controls to help organisations deploy AI solutions that are scalable, measurable, compliant, and trustworthy.
The position works closely with Data Science, Engineering, Product, Security, Legal, Compliance, Risk, and business teams to manage the full AI lifecycle.
Responsibilities include building and maintaining ML deployment pipelines, monitoring model performance, validating data and model quality, documenting model behaviour, assessing model risks, and supporting governance requirements for machine-learning and generative-AI applications.
The role also helps establish responsible-AI practices by evaluating fairness, privacy, explainability, security, robustness, and regulatory considerations.
It investigates model incidents, supports audits and controls, and translates complex technical and risk findings into clear recommendations for technical teams and senior stakeholders.
Qualifications
Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, Statistics, Information Systems, or a related field.Experience in MLOps, machine-learning engineering, data science, model validation, AI governance, model risk, or responsible AI.Strong understanding of machine-learning lifecycle management, including data preparation, training, testing, deployment, monitoring, retraining, and retirement.Experience building or supporting ML pipelines, model-serving environments, feature stores, experiment tracking, CI/CD workflows, or automated testing.Proficiency in Python, SQL, Git, APIs, cloud platforms, and machine-learning development frameworks.Familiarity with tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, Databricks, Docker, Kubernetes, or similar platforms.Ability to monitor model accuracy, drift, bias, data quality, latency, reliability, and business performance.Knowledge of model-risk-management principles, model documentation, validation standards, control testing, and audit requirements.Strong understanding of responsible-AI topics, including fairness, transparency, explainability, privacy, security, accountability, and human oversight.Experience assessing AI risks, documenting model limitations, investigating incidents, and recommending mitigations.Familiarity with data-protection requirements, AI regulations, security practices, and governance frameworks is advantageous.Ability to create clear technical documentation, model cards, validation reports, control evidence, and stakeholder presentations.Strong analytical thinking, attention to detail, problem-solving, and cross-functional stakeholder-management skills.Experience with generative AI, large language models, regulated industries, enterprise AI platforms, or high-impact automated decision systems is an advantage.
