Senior Machine Learning Engineer

You will be part of a multidisciplinary team of ML engineers and data scientists responsible for building AI \& ML solutions and services as part of robust data collection pipelines handling large volumes of unstructured data. Team will focus on building scalable and reliable systems to process and categorize data that is essential for downstream data collection processing.

• *Primary Job Responsibilities**

• **AI \& ML Extraction Contribution:** Build and deliver high\-impact AI/ML solutions focused on extracting structured data from unstructured sources. Ensure outputs improve data quality, coverage, and reliability across data collection pipelines.
• **Technical Execution:** Design, develop, and deploy ML/NLP/LLM\-based extraction systems. Contribute to building scalable, efficient, and production\-grade services with strong focus on accuracy, latency, cost, and robustness.
• **Extraction System Development:** Develop and optimize extraction workflows using techniques such as document parsing, chunking, embeddings, RAG, and LLM\-based extraction methods.
• **Evaluation \& Quality Improvement:** Define and implement evaluation frameworks (precision, recall, F1, field\-level accuracy) and continuously improve extraction performance through iterative experimentation.
• **Data Pipeline Contribution:** Work on high\-throughput data collection pipelines, ensuring seamless integration of extraction components with upstream and downstream systems.
• **MLOps \& Reliability:** Contribute to model deployment, monitoring, logging, and CI/CD pipelines. Ensure models are observable and reliable in production environments.
• **Collaboration \& Stakeholder Alignment:** Partner with Product, Data Collection Engineering, and Platform teams to translate requirements into scalable extraction solutions aligned with business goals.
• **Code Quality \& Knowledge Sharing:** Maintain high standards of code quality, participate in design and code reviews, and share knowledge to improve overall team capability.
• **Innovation \& Continuous Improvement:** Explore and apply advancements in NLP, LLMs, and extraction techniques to improve system performance, scalability, and cost efficiency.
• **Process \& Delivery Efficiency:** Contribute to efficient development cycles by following Agile practices and continuously improving workflows and automation.
• **Hiring \& Onboarding Support:** Support hiring efforts through technical interviews and help onboard new team members via documentation and knowledge sharing.

• *Skills and Qualifications**

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related technical field
• 5\+ years of experience in machine learning engineering or data science, with a strong focus on unstructured data processing and information extraction
• Strong hands\-on experience in NLP and extraction\-focused ML (transformers, embeddings, RAG, LLM, Agentic workflows) with practical experience handling large\-scale unstructured datasets, including preprocessing, chunking, and feature engineering
• Experience building and deploying production\-grade ML/LLM systems for tasks such as document parsing, information extraction, and text processing
• Proficiency in Python and SQL, with experience using standard ML/data libraries such as scikit\-learn, pandas, numpy, and deep learning frameworks like PyTorch or TensorFlow
• Experience with the LangChain and equivalent ecosystem for building and orchestrating LLM\-based extraction workflows is a plus.
• Familiarity with data pipeline and orchestration tools such as Kafka, Airflow, or similar technologies
• Experience with cloud platforms (AWS or GCP) and building scalable, production\-ready systems
• Working knowledge of containerization (Docker) and exposure to Kubernetes is a plus
• Understanding of MLOps practices including model deployment, monitoring, evaluation, and iterative improvement
• Effective communication and collaboration skills, with experience working cross\-functionally with product, engineering, and data teams
• Experience working in fast\-paced, data\-driven environments; prior exposure to financial data or similar domains is a plus

• *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 is an equal opportunity employer.

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.

I10\_MstarIndiaPvtLtd Morningstar India Private Ltd. (Delhi) Legal Entity