Develops enterprise data platforms using Python, SQL, and Databricks, building ETL/ELT pipelines and working with data lakes and lakehouse architectures.

You will build evaluation, experimentation, and observability pipelines using Python and platforms like Braintrust, LangSmith, Arize, and Weights & Biases, plus design LLM-as-judge and human-in-the-loop scoring. The role focuses on AI-native consulting for clients like PayPal and adidas, including production monitoring, quality signal detection, and cost attribution. You'll also wire evaluation gates into CI/CD pipelines for the AI Engineering team.

SQL and Python are used to develop pricing strategies, analyse marketing and conversion funnel performance, and evaluate product expansion opportunities. The role supports a fintech unicorn that revolutionises the credit and savings landscape. Mentoring and coaching junior analysts is also part of the position, though the team’s size is unspecified.

Use Python, scikit-learn, TensorFlow, PyTorch, and geospatial tools like QGIS and ArcGIS. As a Machine Learning Engineer at a technology consultancy, you will design, build, and deploy ML models

You'll work with Python, LLMs, Generative AI, RAG architectures, Agentic AI, Conversational AI, Voice AI, Speech-to-Text and Text-to-Speech technologies. This role designs, builds and deploys AI-powered customer and business solutions across Conversational AI, Agentic AI, Voice AI and intelligent automation for a leading international consumer technology and digital services organisation.

You will develop and deploy LLM-powered applications using PyTorch, SGLang, and PEFT/LoRA, building and fine-tuning embedding and reranking models. You will work on an early-stage platform that combines machine learning with real-world trading workflows to transform specialised financial markets. You will join a small engineering team with direct collaboration alongside founders and traders.

You'll work with Python, FastAPI/Django/Flask, PostgreSQL, cloud platforms (Azure/AWS/GCP), and build scalable REST APIs and data pipelines for an enterprise AI platform used by financial institutions.

Build machine learning models in Python using PyTorch or JAX, working with sparse, noisy time-series data from real industrial operations. As an early ML hire at a startup building physics-informed AI for manufacturing, you will design, train and deploy models that combine data-driven learning with physical constraints.

Build REST and GraphQL APIs and MCP servers in Python (FastAPI, Flask) to expose ML models from Databricks to internal apps, CRM platforms, and AI agents at RBC. Work within the Data Science team to architect the serving layer, handling contract design, authentication, and documentation.

You will work with Python, SQL, AzureML, and XGBoost. You’ll join a small, fast-paced pricing team of 6 at a large insurance company, building alternative pricing models, geospatial models, and residual models from large, messy datasets to improve pricing and customer accessibility.

Use Python, SQL, and production ML pipelines for personalization and recommendations (embeddings, ranking, collaborative filtering) on Muzz, the world's largest Muslim dating and marriage app. You'll also use AI tools, conduct A/B experiments, and work with GCP (BigQuery/VertexAI). The role includes mentoring analysts and data scientists and shaping technical standards.