Manager, Engineering, Data Science and Machine Learning

Morningstar; Mumbai City; Hybrid; INR 4,000,000 - 8,000,000; Full time

Benefits offered by this job

Hybrid work model

Equal opportunity employer

Job summary

Morningstar is seeking an Engineering Manager, AI & ML (Data Collection) to lead a multidisciplinary team of engineers and data scientists. You will design, develop, and maintain scalable data collection pipelines, focusing on unstructured data, NLP, and classifiers, while ensuring security, performance, and reliability.

This role requires deep technical direction and hands-on mentoring across global teams.

Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Data Science, or related field.
• 8+ years in software engineering focused on AI/ML, especially data collection and unstructured data processing.
• 3+ years in leadership role managing individual contributors.
• Strong NLP and ML expertise with hands-on classifiers, LLMs, NLP techniques.

Responsibilities

• Drive AI/ML data collection initiatives aligned with business goals.
• Provide hands-on technical leadership in ML models, data pipelines, and services.
• Lead, mentor, and develop engineers and data scientists across geo-distributed teams.
• Oversee data governance, security, and system integrity for data collection pipelines.

Skills

NLP & ML

Leadership

Data pipelines

Cloud-native

Java/Python/SQL

Kubernetes/Docker

Kafka/Airflow

Education

Bachelor/Master/PhD in CS/DS

Tools

Apache Kafka

Airflow

Snowflake

Kubernetes

Docker

Job description

As an Engineering Manager, AI & ML (Data Collection), you will play a vital role in executing the company’s AI and machine learning initiatives with a strong focus on data collection technologies. This position will require deep technical expertise in unstructured data processing, data collection pipeline engineering, and a hands‑on approach to managing and mentoring engineers. Your leadership will ensure that AI & ML data collection systems are developed and operationalized at the highest standards of performance, reliability, and security. You will be working closely with individual contributors, ensuring that projects align with broader business goals and AI/ML strategies.

This role requires deep engagement in the design, development, and maintenance of AI & ML models, solutions, architecture, and services. You will need to provide strong technical direction, problem-solve complex technical challenges, and ensure that the team consistently delivers high-quality, scalable solutions. You will leverage your deep knowledge in areas such as advanced natural language processing (NLP), generative AI (GenAI) and large language models (LLMs), ML Operations (MLOps), data architecture, data pipelines, and cloud-managed services.

Your leadership will ensure that our AI/ML systems align with global business strategies, maintaining seamless integration and high-performance standards. You will oversee the end-to-end lifecycle of AI/ML data systems—from research and development to deployment and operationalization.

You will be responsible for mentoring team members, resolving technical challenges, and fostering a culture of innovation and collaboration while ensuring they have the right tools, frameworks, and guidance to succeed. This role offers a unique opportunity to drive impactful change in a fast‑paced, dynamic environment, where your efforts will directly contribute to the success of our AI/ML initiatives globally. Your ability to collaborate with cross‑departmental stakeholders, provide leadership across locations, set high standards for the team, and hire, train, and retain exceptional talent is foundational to your success. You will solicit feedback, engage others with empathy, inspire creative thinking, and help foster a culture of belonging, teamwork, and purpose.

You will lead a multidisciplinary team of 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. Your team will focus on building scalable and reliable systems to process and categorize data that is essential for downstream data collection processing.

Outline of Duties and Responsibilities

• AI & ML Data Collection Leadership : Drive the execution of AI & ML initiatives related to data collection, ensuring that the team’s efforts are aligned with overall business goals and strategies.
• Technical Oversight : Provide hands‑on technical leadership in the engineering of ML models and services, focusing on unstructured data, NLP, and classifiers. Oversee and contribute to the implementation of scalable solutions that meet high standards of reliability and efficiency.
• Team Leadership & Development : Lead, mentor, and develop a high-performing team of engineers and data scientists, fostering a culture of innovation and continuous improvement. Ensure effective communication and coordination within your team and across geographically dispersed teams.
• NLP Technologies : Contribute to the development and application of NLP techniques, including classifiers, transformers, LLMs, and other methodologies, to efficiently process and categorize unstructured data. Ensure these models are integrated seamlessly into the broader AI/ML infrastructure.
• Data Pipeline Engineering : Design, develop, and maintain advanced data collection pipelines, utilizing orchestration, messaging, database, and data platform technologies. Ensure pipelines are optimized for scalability, performance, and reliability.
• Cross-functional Collaboration : Work closely with other AI/ML teams, data collection engineering teams, product management, and others to ensure data collection efforts support broader AI/ML goals and product objectives.
• Innovation & Continuous Improvement : Continuously explore and implement new technologies and methodologies to enhance the efficiency and accuracy of data collection and processing systems. Stay at the forefront of advancements in NLP and data processing.
• System Integrity & Security : Ensure that all data collection systems meet the highest standards of integrity, security, and compliance. Implement best practices for data governance and model transparency.
• Talent Acquisition & Retention : Play an active role in recruiting, training, and retaining top engineering talent. Foster an environment where team members are encouraged to innovate, feel valued, and achieve their full potential.
• Process Improvement : Apply Agile, Lean, and Fast-Flow principles to improve team efficiency and the delivery of high-quality data collection solutions.
• Support Company Vision and Values : Model and promote behaviors that align with the company’s vision and values. Participate actively in company‑wide initiatives and projects as required.

Experience, Skills and Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Data Science, or a related field.
• 8+ years of experience in software engineering, with a focus on AI & ML technologies, particularly in data collection and unstructured data processing.
• 3+ years of experience in a leadership role managing individual contributors.
• Strong expertise in NLP and machine learning, with hands‑on experience in classifiers, large language models (LLMs), and other NLP techniques.
• Extensive experience with data pipeline and messaging technologies such as Apache Kafka, Airflow, and cloud data platforms (e.g., Snowflake).
• Expert‑level proficiency in Java, Python, SQL, and other relevant programming languages and tools.
• Strong understanding of cloud‑native technologies and containerization (e.g., Kubernetes, Docker) with experience in managing these systems globally.
• Demonstrated ability to solve complex technical challenges and deliver scalable solutions.
• Excellent communication skills with a collaborative approach to working with global teams and stakeholders.
• Experience working in fast‑paced environments, particularly in industries that rely on data‑intensive technologies (experience in fintech is highly desirable).

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.