Job description

1. JOB DETAILS:
Job Title: Analyst, ML Engineer
Reports to: Sr. Manager, IT Digital Platforms & Digital Transformation
Department: IT Applications
Function: IT Applications
Company: Hikma Holding
2. JOB PURPOSE:

The ML Engineer is responsible for supporting the development, testing, and deployment of machine learning models and AI-powered pipelines across Hikma Pharmaceuticals. Working as part of the AI team under the Sr. Manager, IT Digital Platforms & Digital Transformation, and under the close guidance of the AI Architect and AI developers’ team, this role provides hands-on ML engineering support across the solution lifecycle — from data preparation and model experimentation through to deployment and monitoring. The ML Engineer is expected to develop their machine learning and data engineering skills rapidly within a structured team environment, contributing to Hikma's enterprise AI transformation while building foundational expertise in regulated pharmaceutical AI delivery.

Number of Staff Supervised:

Direct Reports Count:

0 (0 vacant)

Indirect Reports Count:

N/A

Financial Budget (USD):

Supports AI initiative delivery under team supervision

4. KEY ACCOUNTABILITIES:

Description

ML Model Development & Experimentation

• Support the development, training, and evaluation of machine learning models under the guidance of the AI Architect and senior team members, following approved architecture standards and initiative briefs
• Assist in ML experimentation activities including data exploration, feature engineering, model selection, and performance evaluation using standard frameworks and cloud AI services
• Apply foundational ML techniques across classical machine learning, NLP, and generative AI domains relevant to Hikma's business areas including Supply Chain, Quality, HR, and Commercial
• Support the implementation of LLM-based solutions including RAG pipelines, prompt engineering, and embedding-based retrieval under senior technical guidance
• Maintain experiment tracking logs, model versioning records, and reproducibility documentation using tools such as ML flow or Azure Machine Learning
• Produce clear and accurate model development artefacts including experiment summaries, performance reports, and model documentation

Data Engineering & Feature Development

• Assist in building and maintaining ML data pipelines covering data ingestion, transformation, validation, and basic feature engineering for model training and inference workflows
• Support data quality checks, anomaly detection, and dataset preparation activities to ensure ML model inputs meet required standards
• Collaborate with the Data & Analytics team to access and understand available data assets, following data governance and privacy guidelines
• Work with structured data sources including relational databases and enterprise system extracts, developing proficiency in handling diverse data types over time

ML-Ops & Production Support

• Support the implementation and maintenance of ML-Ops pipeline components including model packaging, deployment, and basic performance monitoring under senior team guidance
• Assist in deploying ML models to cloud environments using approved tooling (Azure Machine Learning, ML-flow, Docker, or equivalent)
• Monitor deployed models for observable performance issues and flag anomalies to the AI Architect or senior team members for investigation
• Maintain accurate records in model registries including versioning and change logs across AI initiatives
• Contribute to the documentation and validation support activities for ML models deployed in GxP-regulated contexts, following defined compliance processes

Quality Assurance & Testing

• Support the development and execution of testing activities for ML solutions, including data, pipeline tests, model performance checks, and basic integration testing
• Actively participate in code reviews and technical walkthroughs, applying feedback to improve code quality and engineering practices
• Document assigned ML components clearly including data preparation steps, model configurations, test results, and known issues
• Identify and escalate technical issues encountered across the ML stack in a timely and structured manner