Engineering Manager, Data Science and Machine Learning

About the role

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 requires 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 work closely with individual contributors, ensuring projects align with broader business goals and AI/ML strategies. The role involves designing, developing, and maintaining AI & ML models, solutions, architecture, and services, providing strong technical direction, problem‑solving complex challenges, and ensuring high‑quality, scalable solutions. You will leverage expertise in NLP, GenAI, LLMs, MLOps, data architecture, data pipelines, and cloud‑managed services. Leadership will align AI/ML systems with global business strategies, oversee end‑to‑end lifecycle of AI/ML data systems, mentor team members, foster innovation, collaborate cross‑departmentally, and drive impactful change in a fast‑paced environment.

What you’ll do

• Drive the execution of AI & ML initiatives related to data collection, ensuring alignment with business goals and strategies.
• Provide hands‑on technical leadership in engineering ML models and services, focusing on unstructured data, NLP, and classifiers.
• Lead, mentor, and develop a high‑performing team of engineers and data scientists, fostering innovation and continuous improvement.
• Contribute to the development and application of NLP techniques, including classifiers, transformers, LLMs, and other methodologies.
• Design, develop, and maintain advanced data collection pipelines, utilizing orchestration, messaging, database, and data platform technologies.
• Work closely with other AI/ML teams, data collection engineering teams, product management, and others to support broader AI/ML goals.
• Continuously explore and implement new technologies and methodologies to enhance efficiency and accuracy of data collection and processing systems.
• Ensure all data collection systems meet the highest standards of integrity, security, and compliance.
• Recruit, train, and retain top engineering talent, fostering an environment of innovation and value.
• Apply Agile, Lean, and Fast‑Flow principles to improve team efficiency and delivery of high‑quality data collection solutions.
• Model and promote behaviors that align with the company’s vision and values, participating in company‑wide initiatives.

What you’ll bring

• Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Data Science, or a related field.
• 6+ years of experience in software engineering, focusing on AI & ML technologies, especially 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), Gen AI, RAG, and Agentic AI.
• 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 global system management experience.
• 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 (fintech highly desirable).

Skills

Benefits

• Standard office setting with PC and phone usage.
• Limited corporate travel may be required to remote offices or other business meetings and events.
• Hybrid work environment: four days in‑office each week.
• Tools and resources to engage meaningfully with global colleagues.

Education

Bachelor's