Senior ML Engineer - Generative AI & MLOps (Hybrid)

Zaizi Ltd; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Hybrid working

Loyalty Pension: 5% employer contrib.–

Pension: up to 8%

Life Assurance

2 paid volunteering days per year

5 days paid study leave

Job summary

Zaizi Ltd is seeking a Machine Learning Engineer to research, develop, and test AI algorithms and models that drive automation and insights from data. The role emphasizes building advanced ML models, managing MLOps pipelines, and mentoring junior team members.

Responsibilities include deploying production-ready models, monitoring performance, and tackling non-routine technical challenges within a collaborative, inclusive, hybrid environment.

Qualifications

• Broad technical expertise in machine learning and AI.
• Research, planning, and execution of generative AI research.
• Emerging tech monitoring for generative AI in national security context.
• Prototyping of complex AI applications and proofs of concept.
• Specialist advice as the primary source of generative AI expertise within the org.
• Data science techniques to support model development.

Responsibilities

• Design, build, test, and deploy complex ML models with quality, performance and scalability.
• Design and manage robust MLOps pipelines including CI/CD, monitoring, and retraining.
• Act as technical expert for complex, non-routine ML challenges and innovate solutions.
• Customize, optimise, retrain and maintain existing models.
• Deploy models into production and ensure performance and security requirements.
• Collaborate to integrate models with existing systems and workflows.

Skills

Machine Learning

Generative AI

Data Science

MLOps

Prototyping

Tools

CI/CD

MLOps

Job description

Zaizi Ltd is seeking a Machine Learning Engineer to research, develop, and test AI algorithms and models that drive automation and insights from data. The role emphasizes building advanced ML models, managing MLOps pipelines, and mentoring junior team members.

Responsibilities include deploying production-ready models, monitoring performance, and tackling non-routine technical challenges within a collaborative, inclusive, hybrid environment.