Morningstar
Website:
morningstar.com
Company:
https://www.linkedin.com/company/morningstar
Seniority: Not Applicable
Industries: Financial Services
Job details:
As an Engineering Manager, AI & ML (Data Collection), you will play a critical role in building and scaling the company’s
Unified AI/ML Data Collection Platform, enabling standardized, reliable, and scalable machine learning capabilities across the organization. This role will focus on transforming existing AI/ML and LLM-driven data systems into a cohesive platform that supports data pipelines, model lifecycle management, evaluation frameworks, and production deployment.
This position requires deep technical expertise in machine learning systems, ML platform architecture, and MLOps, along with a strong ability to lead and mentor engineering teams. You will work closely with individual contributors and cross-functional partners to ensure that ML platform capabilities align with broader business objectives and AI/ML strategies.
You will be deeply involved in the design, development, and operationalization of platform components, including data ingestion, feature management, model training and evaluation, and scalable inference systems. You will provide strong technical leadership, solve complex system-level challenges, and ensure delivery of high-quality, reliable, and scalable ML solutions.
Your leadership will ensure that AI/ML systems are production-ready, observable, and maintainable, with a strong emphasis on performance, cost-efficiency, and governance. You will leverage your expertise in areas such as large language models (LLMs), retrieval-augmented generation (RAG), ML Operations (MLOps), distributed systems, and cloud-native architectures.
You will oversee the end-to-end lifecycle of ML systems—from development and experimentation to deployment and monitoring—while ensuring alignment with global engineering standards and business priorities.
You will be responsible for mentoring engineers, driving technical excellence, and fostering a culture of collaboration and innovation. Your ability to partner across teams, influence technical direction, and build high-performing teams will be critical to success in this role.
You will lead a multidisciplinary team of ML engineers responsible for building and maintaining the Unified AI/ML Data Collection Platform. The team focuses on developing scalable systems that support data pipelines, model lifecycle management, LLM-based workflows, and evaluation frameworks, enabling downstream teams to build and deploy AI-driven data collection solutions.
Primary Job Responsibilities
- AI-Powered Data Collection Systems: Lead the design and development of scalable AI-driven data collection and enrichment workflows across structured and unstructured data sources.
- LLM & Generative AI Workflows: Drive the implementation of LLM-based capabilities including extraction pipelines, RAG systems, prompt orchestration, summarization, classification, and automated validation workflows.
- Data Pipeline Engineering: Oversee high-scale ingestion, transformation, and orchestration systems that support real-time and batch data collection processes.
- Data Quality & Evaluation: Establish frameworks for evaluating extraction quality, model performance, hallucination risks, consistency, and overall data reliability.
- Technical Leadership: Provide hands-on leadership in architecture, system design, operational scalability, and engineering best practices for AI-enabled data systems.
- Team Leadership & Mentorship: Build and grow a high-performing engineering team while fostering a culture of ownership, collaboration, and continuous improvement.
- Cross-functional Collaboration: Partner closely with product management, data engineering, research, and business stakeholders to align technical investments with organizational goals.
- Platform Reliability & Governance: Ensure systems meet standards for scalability, observability, security, compliance, and auditability.
- Innovation & Continuous Improvement: Evaluate emerging AI/ML technologies, frameworks, and tooling to improve automation capabilities and developer productivity.
- Operational Excellence: Drive engineering best practices, Agile delivery processes, and operational maturity across the team.
- Hiring & Talent Development: Support recruiting, onboarding, and retention efforts while cultivating an inclusive and high-performing team environment.
- Company Leadership: Model company values and contribute to a culture of innovation, accountability, and collaboration.
Skills And Qualifications
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Data Science, or related field.
- 8+ years of experience in software engineering, with a focus on machine learning systems, ML platforms, or distributed systems.
- 3+ years of experience managing engineering teams and leading technical initiatives.
- Strong experience building production-grade ML systems, including model deployment and lifecycle management.
- Hands-on experience with MLOps tools and practices, including CI/CD, model monitoring, and experiment tracking (e.g., MLflow, W&B).
- Experience with pipeline orchestration and data platforms (e.g., Airflow, Dagster, Kafka, Snowflake).
- Strong programming skills in Python and SQL, or similar languages.
- Experience with cloud platforms and containerization (e.g., AWS/GCP/Azure, Docker, Kubernetes).
- Experience with LLM-based systems in production, including RAG pipelines, embeddings, and vector databases.
- Solid understanding of distributed systems, scalability, and system design trade-offs.
- Proven ability to solve complex technical challenges and deliver scalable solutions.
- Excellent communication and collaboration skills, with experience working across global teams.
- Experience working in fast-paced, data-driven environments.
Working Conditions
The job conditions for this position are in a standard office setting. Employees in this position use PC and phones on an ongoing basis throughout the day. Limited corporate travel may be required to remote offices or other business meetings and events.
Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office 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.
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