In this role, you’ll provide both technical leadership and strategic direction, ensuring your team delivers innovative, production-grade AI features that help global enterprises optimize their cloud and IT investments. You’ll act as a bridge between data science, software engineering, and product management—driving execution while fostering a collaborative, high-performance culture.
Your role and responsibilities
- Lead, mentor, and grow a multidisciplinary team of data scientists, ML engineers, and software developers
- Define technical direction, set priorities, and drive successful execution of AI/ML projects within the product suite
- Collaborate with product and design teams to shape intelligent, customer-focused solutions
- Oversee the full AI/ML development lifecycle, from research and prototyping to scalable deployment and monitoring
- Promote best practices in machine learning engineering, MLOps, and cloud-native software development
- Foster a culture of innovation, ownership, and continuous improvement
- Communicate strategy, progress, and impact to stakeholders across the organization
Required education
Preferred education
Required technical and professional expertise
- Demonstrated experience in data science, software engineering, or applied ML, with at least 2 years in a technical leadership or management role
- Proven experience delivering AI/ML-powered features in a production environment
- Strong technical foundation in machine learning, data architecture, and software engineering
- Proficiency in programming languages such as Python , Java , or Go , and hands-on experience with cloud platforms (AWS, Azure, or GCP)
- Experience managing cross-functional teams and collaborating across engineering, data, and product functions
- Excellent communication and organizational skills
Preferred technical and professional experience
- Experience with FinOps , IT financial management, or tools such as ApptioOne, Cloudability, or Targetprocess
- Familiarity with MLOps tools and practices (e.g., MLflow, SageMaker, Airflow, Kubernetes)
- Exposure to generative AI or large language models (LLMs) in enterprise applications
- Track record of building high-performing teams and scaling data science efforts in a SaaS environment
