Your work profile
As a level in our Consultant/Senior Consultant/Manager you’ll build and nurture positive working relationships with teams and clients with the intention to exceed client expectations: -
We are seeking skilled ML-Ops Manager / Senior Consultants to implement and manage ML-Ops frameworks for various industries. The ideal candidates will have a minimum of 8+ Years (Manager)/4-7 Years of experience (Senior Consultant) in Designing, Implementing, and leading ML-Ops frameworks. In this role, you will be responsible for the deployment, testing, and continuous integration of machine learning models while working with cross-functional teams.
Following are the Key requirements:
Collaborate with data scientists, developers, and infrastructure teams to ensure seamless integration of machine learning models into production environments.
Develop and implement ML-Ops best practices to streamline the end-to-end machine learning lifecycle, from model training and testing to deployment and monitoring.
Design, build, and maintain scalable and reliable machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, and deployment.
Implement and manage continuous integration and continuous deployment (CI/CD) processes for machine learning models.
Monitor the health and performance of deployed models, identifying and addressing issues related to data drift, model degradation, and performance bottlenecks.
Automate model retraining and deployment processes to ensure models remain up to date with changing data and requirements.
Collaborate with cross-functional teams to ensure data security, compliance, and privacy standards are met throughout the ML-Ops lifecycle.
Drive improvements in the machine learning infrastructure by evaluating new tools, technologies, and processes.
Participate in on-call rotations to address critical system issues as they arise.
Additional Preferred Skills:
Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack) for tracking model performance and system health.
Knowledge of machine learning model monitoring and management platforms.
Background in distributed computing and parallel processing.
Domain expertise in Telecom, Retail / BFSI, Marketing, Life science, Manufacturing etc.
Desired qualifications
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
Proven experience in deploying and managing machine learning models in production environments.
Strong programming skills in languages such as Python, and experience with relevant libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Hands-on experience with containerization technologies such as Docker and orchestration platforms like Kubernetes.
Proficiency in designing and optimizing data pipelines for large-scale data processing and machine learning.
Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and services for ML model deployment and management.
Experience with version control systems (e.g., Git) and CI/CD tools (e.g., Jenkins, GitLab CI/CD).
Strong understanding of DevOps principles and practices, and the ability to collaborate effectively with cross-functional teams.
Excellent problem-solving skills and the ability to diagnose and resolve complex technical issues.
Strong communication skills, both written and verbal, to effectively convey technical information to non-technical stakeholders.