AI Engineer Engineer DD&IT US&I

This position is pivotal in identifying and incubating cutting-edge AI technologies that align with the strategic goals of the company, enhancing our capabilities in data-driven decision-making. It is crucial in defining and promoting best practices in AI solution development and deployment.

The AI Engineer, through their forward-thinking mindset, ensures seamless integration of innovative AI solutions into existing frameworks, making sure they are scalable, reliable, and tailored to meet the unique demands of the pharmaceutical industry. The AI Engineer will contribute to our mission of advancing healthcare through technology, ultimately improving patient outcomes and driving business success.

We are looking for an AI Engineer with a strong Data Science / Machine Learning background who is passionate about Large Language Models (LLMs) and agentic AI. In this role, you will design and build AI solutions that combine LLMs, tools, and workflows to solve real business problems in the pharmaceutical industry.

You will:

• Understand complex business needs 
• Rapidly prototype and iterate on AI use cases 
• Build end-to-end POCs and early production solutions 
• Collaborate with other teams for long-term scaling and ownership 

You will help us turn ideas into working AI products that improve how we work and, ultimately, contribute to better outcomes for patients.

Key Responsibilities

• Understand complex and critical business problems, and formulate integrated analytical approaches to mine data sources, employ statistical methods and machine learning algorithms to contribute to solving unmet medical needs, discover actionable insights, and automate processes for reducing effort and time for repeated use.
• Work with business stakeholders to translate problems into practical AI/LLM use cases (e.g., retrieval-augmented generation, agents, copilots, summarization, workflow automation).
• Design, build, and iterate on agentic AI solutions using frameworks such as LangChain, LangGraph, or similar libraries.
• Develop and fine-tune LLM-powered applications (prompt engineering, RAG pipelines, tool calling, orchestration) using internal and/or external data sources.
• Apply traditional Data Science and Machine Learning methods when appropriate (e.g., classification, clustering, recommendation, NLP) and combine them intelligently with LLM-based approaches.
• Implement “good enough” MLOps practices for reliability (versioning, basic monitoring, logging, simple CI/CD) to bring POCs into early production.
• Collaborate with platform, cloud, and MLOps teams to onboard solutions into more robust, long-term platforms when needed.
• Ensure that AI solutions follow internal guidelines around security, privacy, compliance, and responsible AI, especially in a regulated pharma environment.
• Document solutions clearly and share knowledge with other teams (playbooks, examples, reusable components).
• Stay up to date with the latest developments in LLMs, agentic AI, and GenAI tooling, and help identify where new capabilities can create value.

Essential Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field (equivalent practical experience is also valued).
• Solid experience (e.g., 3+ years) in Data Science / Machine Learning: building and deploying models to solve real business problems.
• Hands-on experience developing applications with Large Language Models (e.g., OpenAI, Azure OpenAI, Anthropic, or similar), including:
• Prompt design and evaluation 
• RAG pipelines (vector stores, embeddings) 
• Tool/agent orchestration using frameworks like LangChain, LangGraph, or similar
• Proficiency in Python and common data/ML libraries (e.g., pandas, scikit-learn, PyTorch or TensorFlow, spaCy, etc.).
• Experience building end-to-end prototypes and bringing them to production (e.g., via APIs, simple web apps, or internal tools).
• Some experience with cloud platforms (e.g., AWS, Azure, GCP) and basic MLOps concepts (environments, APIs, monitoring, CI/CD) – comfortable working in that ecosystem in collaboration with specialized teams.
• Strong problem-solving skills, curiosity, and a hands-on, experimental mindset.
• Ability to work effectively in cross-functional teams and communicate technical concepts clearly to both technical and non-technical stakeholders.
• Excellent organizational skills and attention to detail in managing multiple initiatives and evolving solutions.