Senior AI ML Engineer

We're looking for a Senior AI/ML Engineer who thrives at the intersection of research and production someone who doesn't just build models, but ships systems that scale. You'll own the full ML lifecycle: architecting robust data pipelines, training and rigorously evaluating models, and deploying them into high-throughput production environments. This is a role for engineers who want their work to move fast, break assumptions (not systems), and directly shape how AI gets built at scale. If you're energised by turning cutting-edge research into real-world impact, this is your next challenge.

Responsibilities:

• Design, build, and maintain end-to-end ML pipelines from raw data ingestion to model serving with a focus on scalability and reliability.
• Develop, train, and rigorously evaluate ML/DL models using sound experimentation practices (A/B testing, offline/online metrics, statistical validation).
• Own the MLOps lifecycle: implement CI/CD pipelines for model training and deployment, version control for datasets and models, and automated retraining workflows.
• Deploy and monitor models in production using containerised, orchestrated infrastructure (Docker, Kubernetes), ensuring low-latency, high-availability inference.
• Collaborate cross-functionally with Data Engineering, Product, and Backend teams to translate business problems into scalable ML solutions.
• Optimise model performance for cost, latency, and accuracy trade-offs across cloud-based training and inference environments.
• Mentor and guide junior engineers and data scientists, conducting code reviews, sharing best practices, and raising the technical bar of the team.
• Stay current with emerging research (LLMs, generative AI, RAG architectures) and proactively identify opportunities to apply them to existing products.

Requirements:

• Languages: Expert-level Python; working knowledge of R or C++ is a plus.
• Frameworks: TensorFlow, PyTorch, Scikit-Learn, Keras.

Data and Cloud:

• Strong SQL and NoSQL fundamentals.
• Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure), specifically their ML tooling SageMaker, Vertex AI, or equivalent.
• Experience with distributed data processing frameworks: Spark, Hadoop.

MLOps and Deployment:

• Proficiency with Docker and Kubernetes for containerised deployment.
• Experience building CI/CD pipelines for ML workflows.
• Familiarity with experiment tracking and pipeline orchestration tools like MLflow or Kubeflow.

Advanced / Nice-to-Have:

• Practical experience with LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG) architectures.
• Experience fine-tuning transformer-based models for domain-specific use cases.

Qualifications and Experience:

• Bachelor's, Master's, or PhD in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.
• 5+ years of hands-on experience building, training, and deploying production-grade AI/ML models at scale.
• Demonstrated track record of taking models from prototype to production in a real-world business setting.