Machine Learning Engineer

Keyrus is an international consulting firm, specializing in the integration of data intelligence and Digital solutions. With over 3,500 employees spread across 25 countries, Keyrus continues to deliver on such projects to a wide range of clients from various industries including but not limited to Banking/Finance, Healthcare/pharmaceuticals, FMCG, Oil & Gas, and more.

As part of Keyrus’ solution delivery, we are also in a position to recruit and place technical consultants to complement on existing client projects with their expertise. As such, we seek innovative and agile people to support ambitious and forthcoming technological challenges.

We're seeking a Data Engineering Consultant with expertise in Databricks and Generative AI to build scalable data pipelines and integrate LLMs into enterprise workflows. This role combines modern data engineering with AI innovation to support advanced analytics and GenAI solutions.

We are looking for a passionate and skilled Machine Learning / Data Scientist with hands-on experience in Generative AI (GenAI) and Databricks to join our growing AI/ML team.

Key Responsibilities:

• Design and develop advanced machine learning models and GenAI applications (e.g., LLM fine-tuning, embeddings, prompt engineering).
• Implement scalable data pipelines and ML workflows using Databricks and Spark.
• Leverage LLMs (e.g., GPT, LLaMA, Claude) for various use cases including summarization, text generation, knowledge extraction, etc.
• Collaborate with data engineers, product managers, and stakeholders to understand business problems and translate them into technical solutions.
• Monitor model performance, ensure robustness, and manage model lifecycle (training, deployment, retraining).
• Research and stay up to date with the latest trends in GenAI, foundation models, and ML operations (MLOps).

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, AI, or related field.
• 3+ years of hands-on experience in machine learning and data science.
• Strong knowledge of Python, ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch), and GenAI libraries (e.g., HuggingFace Transformers, LangChain).
• Proven experience working with Databricks and Apache Spark in production environments.
• Experience with LLMs and prompt engineering.
• Proficiency in data wrangling, feature engineering, and model evaluation.