
Senior Machine Learning Engineer
- India
- Permanent
- Full-time
- Lead the deployment of ML solutions on the enterprise infrastructure, in line with ML engineering standards and best practices (version control, testing, deployment, maintenance).
- Manage the entire lifecycle of data science/machine learning models, including monitoring, data gathering for retraining, and updates.
- Implement ML engineering best practices, such as coding standards, code reviews, and automated testing.
- Define and implement metrics to evaluate the functional performance and computational resource efficiency of ML and AI components.
- Can coordinate and manage competing priorities across a portfolio of projects.
- Ability to influence across organisations, proven collaboration skills, comfortable working with ambiguity, collaborate with cross-functional teams, including data scientists, data engineers, and business stakeholders.
- Contribute to a highly collaborative team with a culture of ownership, initiative and responsibility.
- Contribute to the development of our Team’s ML engineering standards of reusable DS, ML, and AI assets.
- Motivate, coach, mentor colleagues within the ML Engineering Team to develop technical excellence.
- Manage a team of up to 3 Machine Learning Engineers
- BSc, MSc or PhD degree in mathematics, computer science, or another scientific discipline that provides solid foundations on relevant aspects of Data Science.
- 5-10 years of industry experience with proven experience implementing machine learning engineering pipelines on large datasets.
- Strong understanding of ML engineering best practices.
- Strong collaboration skills and comfortable working with ambiguity, making quick, informed decisions considering trade-offs.
- Experience with deploying machine learning solutions on enterprise infrastructure.
- Strong programming skills in Python or similar programming languages..
- Ability to manage competing priorities across a portfolio of projects.
- Strong experience with cloud-based infrastructure and distributed computing systems, such as Azure, AWS, or GCP.
- Experience with containerization and orchestration tools, such as Docker and Kubernetes.
- Understanding of software engineering principles, such as modular design, clean code, and testing.
- Familiarity with DevOps practices and tools, such as continuous integration and deployment (CI/CD) pipelines and configuration management tools like Ansible or Terraform.
- Excellent problem-solving skills and the ability to debug complex issues in production environments.
- Strong communication skills and the ability to collaborate with cross-functional teams.
- Familiarity with monitoring and logging tools, such as Grafana, Prometheus, and ELK Stack.
- Knowledge of security best practices in machine learning engineering.