Become a MindWiser!

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Experience: 6+ Years

Location: Remote

Type: Full Time

Role Description

• AI architect will be responsible to design, build, deploy & continuously optimize cloud native Machine Learning system.
• You would work closely with other Data & ML Engineers to design and develop data/ML pipelines for continuous training of various AI/ML models.
• As part of architecture development, you will work together with Data Scientists, data engineers and model development team to enable the ML Model end-to-end lifecycle i.e., from Commissioning training datasets, deploying it in PROD environment & implement model drift by following best practices in model monitoring.
• As part of the design and architecting principles, will ensure seamless deployment and efficient re-use in multi project variant scenarios with focus on ensuring cost-effective deployments.
• As part of the job responsibility, will also enable the downstream users and core development team to define common touch point for integration.
• As part of the design principles, you will ensure automation at all steps of validation.

Qualifications

• Master's degree/ bachelor's degree in computer science or Information Science or equivalent engineering stream.
• Experience: Overall 6+ years of experience, including DevOps with 6+ years experience in Machine Learning Engineering & 3+ years experience in leading a team of at least 3 members

Additional Information

Mandatory Skills & experience:

• Strong programming skills in Python.
• Hands-on experience with cloud native MLOps frameworks and services (AWS).
• Good SQL knowledge.
• Experience with CI/CD practices, DevOps, and MLOps principles.
• Experience with DevOps, data pipelines, AWS ECR registry, deployment methods viz. Docker, K8s, etc.
• Good knowledge of ML model development and deployment life cycle.

Preferred Skills:

• Experience with functional and system integration testing.
• Experience working with Agile methodologies and frameworks.
• Hands-on experience with scaling using containerization in a cloud environment (AWS).
• Functional understanding of core data engineering concepts – data management, data lakes, data analysis and data lifecycle.
• Motivating attitude, strong communication, interpersonal skills, structured and analytical thinking.