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Medium—metro location and established brand increase interest, but senior ML platform specialization reduces candidate pool.
Medium—MLOps and distributed-systems skills transfer across industries, but LLM and data-collection domain add bias.
High—explicit seniority, management requirement, and numerous mandatory ML platform and cloud skills.
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Lead design and development of scalable AI/ML data collection platform supporting data pipelines, model lifecycle management, evaluation, and production deployment.
Provide technical leadership and mentorship to engineering teams building ML systems including LLM-based workflows, data quality frameworks, and scalable inference systems.
Ensure platform reliability, operational excellence, compliance, and alignment with business objectives through cross-functional collaboration and continuous improvement.
Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Data Science, or related field.
8+ years in software engineering with focus on ML systems, ML platforms, or distributed systems.
3+ years managing engineering teams and leading technical initiatives.
Strong hands-on experience with production-grade ML systems, MLOps tools (e.g., MLflow, W&B), pipeline orchestration (e.g., Airflow, Kafka), cloud platforms (AWS/GCP/Azure), Python and SQL programming, and LLM-based systems in production.
Experienced leader capable of managing and growing high-performing multidisciplinary ML engineering teams.
Technical expert in ML platform architecture, MLOps, distributed systems, and scalable production deployments.
Strategically aligned with building unified AI/ML platforms integrating LLM workflows and data engineering to drive organization-wide machine learning capabilities.