Senior AI / ML Engineer

As a Senior AI/ML Engineer, you will lead the delivery of scalable AI/ML solutions to business problems. You will build, deploy, scale and maintain AI/ML solutions. You will apply engineering best practices, implement rigorous evaluation frameworks, and design MLOps and observability standards. You will be the technical authority for ML engineering challenges, from setting up model training and fine-tuning to architectures and system design for serving AI/ML inference solutions in production. You will help drive AI/ML engineering excellence through mentorship, design reviews, and platform investment. In this role, you will own technical delivery and partner with applied scientists, software engineers, and product teams to realise AI capabilities into production.

Responsibilities:

• Lead delivery of applied AI/ML solutions, including data pipelines, model training and experimentation infrastructure, evaluation systems, production-ready pipelines and APIs, and ML Ops for monitoring models or solutions in production.
• Define ML engineering standards for model development, evaluation, and deployment; implement reusable training pipeline templates.
• Design and implement model evaluation systems and tooling including benchmark suites, human evaluation workflows, and online experiment platforms in partnership with applied science teams.
• Lead architecture and engineering of LLM and GenAI systems including RAG pipelines, fine-tuning infrastructure, and agentic frameworks.
• Build and maintain AI observability frameworks covering model performance, data drift, training health metrics, and responsible AI monitoring.
• Build and operate distributed training pipelines for advanced ML and GenAI models.
• Implement scalable model serving architectures for real-time and batch inference.
• Develop reusable MLOps components to support experimentation, deployment, monitoring, and rollback.
• Partner with AI/ML scientists to productionize models while meeting accuracy, performance, reliability, and responsible AI requirements.

Requirements:

• Bachelor's or master's degree in computer science, machine learning, or related technical field; Master's or equivalent experience beneficial.
• 8-12 years of experience building and delivering AI/ML solutions into production.
• Demonstrated ability to define software engineering standards for AI/ML systems across the domain including code quality, testing requirements, service design patterns, and API contract guidelines.
• Demonstrated ability to define model implementation and training standards including architecture patterns, evaluation criteria, and responsible AI assessment frameworks adopted across the domain.
• Demonstrated ability to define ML Ops platform standards and reusable deployment templates adopted across the domain.
• Experience with common ML tools and frameworks and implementation such as Python, Spark, Airflow, MLFlow, feature stores, cloud ML platforms (Aws/Azure).

Must haves:

• Acknowledge the presence of choice in every moment and take personal responsibility for your life.
• Possess an entrepreneurial spirit and continuously innovate to achieve great results.
• Communicate with honesty and kindness and create the space for others to do the same.
• Lead with courage, knowing the possibility of greatness is bigger than the fear of failure.
• Foster connection by putting people first and building trusting relationships.
• Integrate fun and joy as a way of being and working, aka don't take yourself too seriously.