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.
3. 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
4. Behavioural Competencies:
Name - Level
Initiative & Drive for Results - Very Good
Change & Innovation - Very Good
Communication & Influence - Good
Developing & Empowering others - Good
Problem Solving & decision Making - Excellent
Strategic Thinking - Good
5. Technical Competencies:
Name - Level
ML Model Development & Experimentation - Good
Python & ML Frameworks (PyTorch, TensorFlow, scikit-learn, HuggingFace) - Good
MLOps & Model Lifecycle Tooling - Good
Data Engineering & Pipeline Support - Good
Cloud AI/ML Services (Azure ML) - Good
Generative AI & LLM Development (RAG, LangChain, Semantic Kernel) - Very Good
Model Explainability & Responsible AI Awareness - Very Good
Containerization & CI/CD Basics (Docker, Git, Azure DevOps) - Good
6. COMMUNICATIONS & WORKING RELATIONSHIPS:
Internal
- AI Champions Network
- AI Architect
- AI Developer
- Data & Analytics Team
External:
- AI/ML Vendors & Solution Providers
- Implementation Partners
- Open Source & Developer Communities
7. QUALIFICATIONS, EXPERIENCE, & SKILLS:
- Bachelor’s degree in computer science, Software Engineering, Data Science, Mathematics, Statistics, or related technical field *(Required)*
- Master's degree in Artificial Intelligence, Machine Learning, Data Science, or Computer Science *(Preferred)*
- Microsoft Azure AI Engineer Associate, AWS Machine Learning Specialty, or Google Professional ML Engineer certification *(Preferred)*
- Relevant ML/AI certifications (e.g., DeepLearning.AI ML Specialization, Databricks ML Associate, fast.ai) *(Preferred)*
- 0–1 year of professional experience in software development, data science, machine learning, or a related technical field (fresh graduates are welcome to apply)
- Demonstrated hands-on experience with ML model development through academic projects, internships, capstone projects, hackathons, or personal projects
- Familiarity with cloud AI/ML platforms (Azure, AWS, or GCP) gained through coursework, self-study, or practical experimentation *(Preferred)*
- Any exposure to generative AI, LLM tools, or RAG concepts through academic or personal projects *(Preferred)*
- Pharmaceutical, healthcare, life sciences, or other regulated industry exposure *(Preferred but not expected)*
- Working knowledge of Python and foundational ML/DL libraries including scikit-learn, pandas, NumPy, and at least one deep learning framework
- Practical exposure to training and evaluating ML models including classical machine learning and at least one of: NLP, computer vision, or time-series analysis — through academic, personal, or internship projects
- Basic familiarity with generative AI concepts including Large Language Models, prompt engineering, and RAG principles *(Preferred)*
- Awareness of ML-Ops concepts and tools such as ML-flow or Azure Machine Learning for experiment tracking and model management *(Preferred)*
- Basic experience with data preparation, cleaning, transformation, and exploratory data analysis using Python
- Familiarity with version control using Git and basic software development practices including code reviews and testing
- Basic understanding of cloud platforms (Azure, AWS, or GCP) and awareness of cloud-based AI/ML services *(Preferred)*
- Familiarity with containerization concepts (Docker) and CI/CD basics *(Preferred)*
- Awareness of responsible AI principles, data privacy regulations (GDPR, HIPAA), and IT security practices relevant to ML development
- Any familiarity with pharmaceutical business processes or GxP compliance in a technology context is a plus *(Preferred)*
