Senior Full Stack AI/ML Engineer - 6 month FTC

Aztec Group

About The Position

The Aztec Group are seeking an experienced Senior Full Stack AI/ML Engineer to join our team on a six-month contract. This role will focus on operationalising, optimising, supporting, and enhancing AI-driven solutions that embed models developed initially by a third-party provider. The successful candidate will also establish observability, continuous evaluation, CI/CD pipelines, and other best practices for production-grade AI systems. Additionally, the role includes knowledge transfer and capability building to help establish a permanent internal AI/ML engineering function.

Requirements

7-10 years of Full Stack Software Engineering experience essential
3 years AI/ML Engineering specifically essential
Proven experience in deploying and operationalising machine learning models in production environments
Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch) and MLOps tools (MLflow, Kubeflow, or similar)
Experience with CI/CD pipelines, containerisation (Docker), and orchestration (Kubernetes)
Familiarity with observability tools and practices for AI systems
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools (Terraform)
Knowledge of data pipelines and integration with enterprise systems
Strong problem-solving and analytical skills
Excellent communication and stakeholder management abilities
Ability to work independently and deliver results within tight timelines
You will need to be quick to learn new systems and be great with people, as close working relationships between our colleagues and clients is at the heart of what we do.

Nice To Haves

Financial services or alternative asset management experience is highly desirable

Responsibilities

Operationalisation & Deployment: Integrate and deploy AI/ML models into production environments with robust monitoring and observability
Establish CI/CD pipelines for model updates and ensure reproducibility and scalability
Optimization & Enhancement: Improve performance, reliability, and efficiency of existing AI solutions
Implement continuous evaluation frameworks to monitor model drift and maintain accuracy
Infrastructure & Tooling: Design and implement infrastructure for model serving, versioning, and lifecycle management
Ensure compliance with data governance, security, and regulatory requirements
Knowledge Transition & Capability Building: Document processes, frameworks, and best practices for internal teams
Mentor internal staff and assist in defining the roadmap for a permanent AI/ML engineering capability
Collaboration & Stakeholder Engagement: Work closely with Data Scientists, Data Engineers, and business stakeholders to align technical solutions with strategic objectives
Communicate technical concepts clearly to non-technical audiences