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.

Experience
3-7 yrs