Principal AI Engineer

As a Principal AI Engineer at Morningstar Sustainalytics, you will play a key role in developing advanced AI solutions for applications in Environmental, Social, and Governance (ESG) domain. Your work will focus on areas such as:

• Extracting information automatically from unstructured documents
• Building Natural Language Generation (NLG) systems
• Developing text classification models

You will collaborate closely with a cross-functional team, including QA specialists, MLOps engineers, and Business Analysts, to drive innovation through ongoing experiments and proof-of-concept (POC) projects, leveraging cutting-edge AI technologies.

Responsibilities:

•  Lead the development of production-ready machine learning models to solve real-world challenges, enabling analysts to make faster, more informed decisions while extracting meaningful insights from data.
• Process and prepare data through cleaning, transformation, feature engineering, and augmentation to optimize model performance.
• Design, fine-tune, and adapt machine learning models to address our unique data requirements.
• Propose new ML architectures and methodologies tailored to our evolving business requirements.
• Collaborate closely with cross-functional teams, including Business Analysts, , Software Architects, Quality Assurance and MLOps Engineers, to design, implement, and scale impactful AI solutions.
• Deliver flexible, incremental solutions in a dynamic environment, ensuring they align with evolving requirements.
• Drive continuous innovation and create Proof-of-Concepts (PoCs) to explore and integrate emerging AI technologies.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of relevant experience in the field.
• Proven track record in developing machine learning projects.
• Proficiency in Python and with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, scikit-learn etc.
• Expertise in model training, fine-tuning, and adaptation of open-source models (e.g., Transformers, LLMs),  with hands-on experience in transfer learning and domain adaptation.
• Proven experience in deploying and monitoring machine learning models in production environments.
• Experience working with diverse data types (e.g., tabular, text, images), along with advanced preprocessing, feature engineering, and data augmentation capabilities.
• Familiarity with cloud platforms like AWS and Azure.
• Strong communication and documentation abilities, including the capacity to explain complex technical concepts to non-technical stakeholders.
• Proficiency in documenting experiments to ensure reproducibility and knowledge sharing.

Some of the benefits you'll have:

• Competitive compensation package and bonus plan
• Public transport and gym reimbursement
• Annual development budget
• Hybrid flexibility: 3 times per week office work.

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Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. We’ve found that we’re at our best when we’re purposely together on a regular basis, at least three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you’ll have tools and resources to engage meaningfully with your global colleagues.