Machine Learning Engineer - (Senior to Staff level)

Opportunity Overview

Machine Learning Delivery

• Design, develop, and productionise advanced machine learning and deep learning systems.
• Develop solutions for forecasting, classification, anomaly detection, risk modelling, and optimization.
• Build automated pipelines for training, fine-tuning, evaluation, versioning, deployment, and retraining.
• Design structured and unstructured data processing pipelines.

LLM and NLP Systems

• Build and deploy LLM and NLP pipelines with fine-tuning, semantic search, and retrieval-augmented generation.
• Deploy and scale large language models using vLLM, Triton, TGI, or comparable inference frameworks.
• Improve inference using quantisation, distillation, pruning, and distributed or multi-GPU techniques.

Production Infrastructure

• Containerise and orchestrate ML workloads using Docker and Kubernetes.
• Implement reproducible ML workflows using MLflow, Kubeflow, or equivalent tools.
• Monitor production models for performance, drift, latency, throughput, and resource utilization.
• Build reliable CI/CD and infrastructure automation for ML systems.

Collaboration and Engineering

• Work with researchers, data scientists, software engineers, and domain experts to transform prototypes into production solutions.
• Develop systems where security, reliability, explainability, and engineering rigor are critical.

Required Qualifications

• 5+ years of experience in Machine Learning Engineering, MLOps, ML Infrastructure, or a closely related field.
• Strong track record deploying machine learning models into production.
• Expert-level Python proficiency.
• Strong knowledge of PyTorch, TensorFlow, Scikit-learn, or comparable modern ML frameworks.
• Experience with transformer architectures and modern NLP or GenAI ecosystems.
• Hands-on Docker, Kubernetes, and production ML orchestration experience.
• Experience with MLflow, Kubeflow, SageMaker Pipelines, or comparable MLOps platforms.
• Strong understanding of model serving, inference optimization, monitoring, lifecycle management, and scalable pipelines.
• Strong software engineering and algorithmic fundamentals.

Advantageous Experience

• Production deployment of large language models.
• RAG, semantic search, embeddings, document intelligence, or transformer fine-tuning.
• Time-series forecasting, anomaly detection, or predictive modelling.
• AWS ML infrastructure including SageMaker, EC2, or EKS.
• Secure or on-premise ML environments, distributed training, ML-focused CI/CD, Infrastructure as Code, or C/C++ experience.

Role Context