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AI & Machine Learning Manager
Unison Group. Engage business stakeholders and identify high-value AI opportunities.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI/ML engineering, including hands-on knowledge of machine learning, deep learning, and Generative AI. Proficient in leading technical teams through the delivery of scalable AI solutions while managing stakeholder expectations and technical trade-offs.
Highest-signal resume keywords
AI/ML EngineeringPython ProgrammingMLOps ConceptsGenerative AI ApplicationsStakeholder Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningNatural Language ProcessingPredictive ModellingModel EvaluationModel ValidationModel DeploymentModel Drift ManagementTechnical DesignDelivery Planning
Soft Skills
Problem-SolvingCommunicationDocumentationDelivery ManagementTeam Leadership
Tools & Technologies
PyTorchTensorFlowScikit-learnExperiment Tracking ToolsModel Registry ToolsCI/CD Tools
Industry Keywords
Artificial IntelligenceData ScienceEnterprise Technology DeliveryCross-Functional TeamsTechnical Trade-Offs
Tech Stack
Tools & technologiesPythonPyTorchScikit-LearnTensorflow
About the role
Key responsibilities & impact- Engage business stakeholders and identify high-value AI opportunities.
- Shape solution architecture for secure, scalable and production-ready AI solutions.
- Lead end-to-end delivery through development, deployment and adoption.
- Review designs, challenge implementation choices and guide teams on engineering standards.
- Guide engineering teams through AI/ML solution delivery.
- Define functional and non-functional requirements and convert them into technical designs and delivery plans.
- Manage stakeholder expectations, explain technical trade-offs and present solution recommendations.
- Support model evaluation, validation, deployment, monitoring, model drift management and lifecycle management.
- Guide MLOps activities including experiment tracking, model registry, CI/CD, deployment and production monitoring.
Requirements
What you’ll need- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related discipline.
- Substantial professional experience in AI/ML engineering, data science, Generative AI or enterprise technology delivery, including experience leading technical teams or complex initiatives.
- Strong hands-on knowledge of machine learning, deep learning, NLP, predictive modelling and Generative AI.
- Strong programming proficiency in Python and practical experience with AI/ML libraries and frameworks such as PyTorch, TensorFlow or scikit-learn.
- Practical experience designing and delivering LLM-based applications, including prompt engineering, RAG, embeddings, vector search and AI agent patterns.
- Working knowledge of model evaluation, validation, deployment, monitoring, model drift and lifecycle management.
- Experience with MLOps concepts and tools for experiment tracking, model registry, CI/CD, deployment and production monitoring.
- Ability to elicit and analyse business requirements, define functional and non-functional needs, and convert them into technical designs and delivery plans.
- Experience working with senior stakeholders and cross-functional teams, managing expectations, explaining technical trade-offs and presenting solution recommendations.
- Strong problem-solving, communication, documentation, delivery management and team leadership skills.