Machine Learning Engineer I and II

We're looking for talented Machine Learning Engineers to build and deploy scalable ML solutions powering real-time personalisation, recommendation, and ranking systems serving millions of users.

Requirements:

• Strong understanding of Statistics, Probability, Linear Algebra and ML fundamentals.
• Hands-on experience with Regression, Classification, Clustering, Recommendation and Time Series.
• Feature engineering, data preprocessing and model evaluation.
• Knowledge of AUC, NDCG, Precision/Recall and F1
• Experience developing and deploying ML models into production.
• Model observability, logging, monitoring and debugging.
• Strong Python with production-ready coding skills.
• Strong SQL - CTEs, Window Functions, Conditional Aggregation and analytical queries.
• Exposure to Airflow, Prefect or similar orchestration tools.
• Exposure to Cloud platforms and basic MLOps.
• Understanding of ETL and Medallion Architecture.

Good to Have:

• Feature Stores and online/offline feature serving.
• Real-time model serving and latency-sensitive inference.
• Databricks / Snowflake.
• Retail domain experience.
• Personalisation and Recommendation use cases.

Key Skills:

• Strong expertise in the end-to-end ML lifecycle.
• Experience building scalable, production-grade ML pipelines serving real users at scale.
• Model observability, drift detection, retraining strategies and performance optimisation.
• Strong Data Engineering fundamentals - ETL and Medallion Architecture.
• Hands-on MLOps - MLflow, GitLab CI/CD or similar.
• Cloud platforms and distributed ML environments.
• Strong Python - ability to solve medium-difficulty coding/data manipulation problems.
• Strong SQL - Window Functions, Self-Joins, Conditional Aggregation, Date Arithmetic and performance reasoning.
• Docker and model serving - Triton, TorchServe, ONNX, FastAPI or similar.
• Feature Stores / online feature serving - Feast, Redis, DynamoDB, Tecton or similar.
• API development using FastAPI, Flask, Django or similar.
• Ability to translate business problems into ML/AI solutions.
• Ability to mentor junior engineers and drive technical discussions.

Good to Have:

• Generative AI / LLM ecosystem exposure.
• Prompt Engineering, RAG, Embeddings and Vector Databases.
• Recommendation ranking models - Two-Tower, GBDT Rankers, Wide and Deep.
• Kafka / Kinesis for real-time feature computation.
• A/B testing and online experimentation.
• Retail and Personalisation domain expertise.