TL;DR

Lead Ml Engineer (Marketing): Developing and deploying high-impact ML model pipelines to improve marketing performance and customer engagement with an accent on robust model architectures, MLOps practices, and scalable automation. Focus on designing end-to-end ML lifecycle solutions, infrastructure-as-code, and mentoring engineering teams to drive transformative business impact at a global scale.

Company

%hirify_global% is a leading SaaS company pioneering enterprise AI and CRM solutions, serving 90% of the Fortune 500.

What you will do

Define and drive technical ML strategy emphasizing model robustness and MLOps best practices
Own the ML lifecycle including governance, testing, and incident response for production systems
Implement CI/CD pipelines, infrastructure-as-code, and automation for ML workflows
Design monitoring solutions for model performance, data quality, and system health
Collaborate with Data Science, Data Engineering, and Product teams to deliver scalable ML solutions
Mentor junior engineers and lead ML engineering best practices

Requirements

8+ years of experience building and deploying ML pipelines at scale with marketing focus
MS or PhD in Computer Science, AI/ML, Software Engineering, or related field
Expertise in AWS services, especially SageMaker
Proficiency in Python and ML frameworks like TensorFlow and PyTorch
Experience with containerization (Docker) and workflow orchestration (Apache Airflow)
Strong knowledge of MLOps, CI/CD, model monitoring, and big data technologies (Snowflake, Spark)