ML Engineer — AI & Data Practice

About the Role

Our AI & Data practice is growing fast and we need engineers who can take models from prototype to production. You'll work on LLM integrations, data pipelines, and ML infrastructure for enterprise clients across finance, healthcare, and logistics.

What You'll Do

• Design and build production ML pipelines and model serving infrastructure
• Integrate LLMs (OpenAI, Claude, open-source) into client products via RAG and fine-tuning
• Build and maintain data pipelines using Python, Spark, or dbt
• Instrument models for monitoring, drift detection, and automated retraining
• Collaborate with data scientists to productionise research prototypes

What We're Looking For

• 3+ years in ML engineering or a related data engineering role
• Strong Python skills; experience with PyTorch or TensorFlow
• Hands-on experience with LLM APIs and prompt engineering
• Familiarity with data pipeline tools (Airflow, Prefect, dbt, Spark)
• Experience deploying and serving ML models in production environments

Nice to Have

• Experience with vector databases (Pinecone, Weaviate, pgvector)
• Knowledge of MLOps platforms (MLflow, Weights & Biases)
• Familiarity with Kubernetes for model serving