Title:  Analyst, ML Engineering

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. JOB DIMENSIONS:

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

5. 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

6. 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

7. 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

8. 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)*