About the role
NeuralForge AI Ltd is seeking a Senior Machine Learning Engineer to design, develop, deploy, and optimise machine learning systems that solve real-world business problems. The successful candidate will work closely with data scientists, software engineers, product managers, and AI leadership to transform research and prototypes into reliable, scalable production solutions. This role requires strong programming skills, practical model development experience, sound understanding of model evaluation, and the ability to build maintainable machine learning pipelines. Success will be measured through model performance, system reliability, deployment efficiency, and measurable business impact.
Benefits & perks
Health Insurance
Private healthcare coverage supporting physical and mental wellbeing.
Flexible Work
Hybrid working arrangements with flexibility around work location and schedules.
Paid Time Off
Paid annual leave to support rest and work-life balance.
Learning & Budget
Dedicated budget for AI/ML courses, technical certifications, conferences, and professional development.
Retirement Plan
Employer-supported workplace pension scheme.
Wellness / Stipend
Wellbeing allowance for approved health, fitness, and wellness activities.
- 01* Design, develop, train, and deploy machine learning models for business and product applications.
- 02* Build scalable data processing, feature engineering, and model training pipelines.
- 03* Collaborate with data scientists to translate experimental models into production-ready solutions.
- 04* Evaluate model performance using appropriate metrics, validation techniques, and testing methods.
- 05* Implement model monitoring, drift detection, versioning, and retraining workflows.
- 06* Optimise model inference performance, scalability, reliability, and resource utilisation.
- 07* Integrate machine learning models into applications and services through APIs and backend systems.
- 08* Apply responsible AI practices, including bias evaluation, explainability, and model limitations assessment.
- 09* Maintain technical documentation covering datasets, experiments, models, and deployment processes.
- 10* Investigate model failures and production issues, implementing corrective actions.
- 11* Contribute to code reviews, engineering standards, and knowledge sharing across the AI/ML team.
- 12* Stay informed about emerging machine learning frameworks, architectures, and deployment practices.
What we evaluate against.
Applied Technical Depth
Must have
- Build and maintain reliable data pipelines for model training, validation, and inference.
- Demonstrate strong Python programming skills and familiarity with relevant ML frameworks and data processing tools.
- Identify data quality issues, leakage risks, and pipeline bottlenecks before they affect production systems.
Nice to have
- Data pipelines produce consistent and validated outputs.
- Code follows maintainability, testing, and engineering standards.
- Data quality issues and pipeline failures are identified and addressed promptly.
Problem Decomposition
Must have
- Deploy, version, monitor, and maintain machine learning models in production environments.
- Implement appropriate monitoring for model performance, data drift, service health, and inference latency.
- Collaborate with platform and software engineering teams to improve deployment automation and operational reliability.
Nice to have
- Model deployments are reproducible and follow documented release processes.
- Monitoring detects relevant model or service degradation.
- Production issues are investigated, resolved, and documented with appropriate follow-up actions.
Customer & Domain Discovery
Must have
- Deploy, version, monitor, and maintain machine learning models in production environments.
- Implement appropriate monitoring for model performance, data drift, service health, and inference latency.
- Collaborate with platform and software engineering teams to improve deployment automation and operational reliability.
Nice to have
- Model deployments are reproducible and follow documented release processes.
- Monitoring detects relevant model or service degradation.
- Production issues are investigated, resolved, and documented with appropriate follow-up actions.
Product & Solution Judgment
Must have
- Design controlled experiments to compare model approaches and validate improvements.
- Apply appropriate evaluation techniques to assess model accuracy, robustness, and generalisation.
- Consider bias, explainability, privacy, and model limitations throughout the development lifecycle.
Nice to have
- Experiments use defined metrics and reproducible methods.
- Model changes are supported by documented evaluation results.
- Relevant risks, limitations, and responsible AI considerations are assessed before deployment.
Execution Under Ambiguity
Must have
- Work effectively with data scientists, product managers, software engineers, and business stakeholders.
- Translate technical findings into actionable recommendations and delivery plans.
- Manage dependencies, communicate risks, and deliver production-ready ML capabilities against agreed priorities.
Nice to have
- Deliverables meet agreed scope and timelines.
- Technical decisions and dependencies are communicated clearly.
- Stakeholders can understand model capabilities, limitations, and business impact.
Team Structure
Cross-functional collaboration with data scientists, AI engineers, software developers, and product teams.
Operating Rhythm
Weekly sprint planning, model performance reviews, technical discussions, and progress updates.
Collaboration Style
Data-driven collaboration focused on experimentation, knowledge sharing, and joint problem-solving.
Communication
Clear, evidence-based communication of model performance, technical risks, and recommendations.
Decision Making
Decisions supported by model evaluation results, experimental evidence, data quality, and business requirements.
Documentation Culture
Documented experiments, model versions, evaluation metrics, technical decisions, and implementation processes.
Ownership
Employees take responsibility for AI initiatives, technical decisions, and measurable business outcomes.
Feedback
Teams share constructive feedback through model reviews, code reviews, and product retrospectives.
Transparency
AI decisions, model limitations, experiment results, and project risks are communicated openly.
Innovation
Teams explore emerging AI/ML technologies to build useful, scalable, and differentiated products.
Learning Culture
Employees continuously develop skills in machine learning, data science, model evaluation, and responsible AI.
Collaboration
Data scientists, ML engineers, software engineers, and product teams work together toward shared goals.
First 30 days
Understand NeuralForge AI's product portfolio, data environment, existing ML systems, engineering standards, and model deployment processes. Establish relationships with data science, software engineering, product, and AI leadership teams. Review existing models, pipelines, evaluation practices, and operational risks, and identify opportunities for improvement.
90 days
Deliver an agreed machine learning improvement or production capability, strengthen model evaluation and monitoring practices, improve the reliability of relevant data or training pipelines, and establish clear documentation for model performance, limitations, and deployment procedures.
Outcomes
Build and maintain reliable, scalable, and measurable machine learning capabilities that deliver sustained business value. Improve model quality, deployment efficiency, monitoring, and operational reliability while promoting reproducible experimentation, responsible AI practices, and strong collaboration across engineering and product teams.