ML Ops

Key Responsibilities:

• Model Lifecycle Management: Support the development, training, deployment, and monitoring of machine learning models in production environments.
• Automation & Optimization: Automate the deployment and provisioning of machine learning models using tools like Docker, Kubernetes, and CI/CD pipelines.
• Monitoring & Observability: Implement monitoring and observability solutions using tools like the ELK Stack to ensure the health and performance of deployed models.
• Data Management: Work with data manipulation and transformation processes, including SQL-based solutions, to streamline data for model training and inference.
• Collaboration: Collaborate with data scientists, software engineers, and cloud engineers to ensure the success of the project.
• MLOps Best Practices: Utilize MLOps architecture and practices to manage the machine learning pipeline effectively.

Must-Have Skills:

• ML Project Experience: Proven work experience in machine learning projects, with hands-on involvement in model lifecycle management.
• MLOps Tools & Frameworks: Proficiency with tools such as Apache Airflow, sklearn, MLFlow, and TensorFlow.
• Cloud Environment Experience: Solid experience in cloud platforms, preferably Google Cloud Platform (GCP).
• Programming Skills: Proficiency in Python for machine learning model development.
• Monitoring Expertise: Experience with monitoring tools such as the ELK stack.
• Software Engineering Practices: Familiarity with versioning, testing, documentation, and code review practices.
• Automation Tools: Expertise in deployment automation tools like Docker, Kubernetes, Openshift, and CI/CD pipelines.

Nice-to-Have Skills:

• Distributed Systems & Data Pipelines: Experience with distributed systems and large-scale data processing technologies such as S3, Spark, Kafka, and Flink.
• Advanced Analytics & Data Science: Affinity with advanced analytics, NLP, and data science workflows.
• ETL Pipeline Development: Hands-on experience building complex ETL pipelines.
• System Design: Experience in system design and architecture for scalable machine learning solutions.
• Linux & Scripting: Proficiency in Bash scripting and Linux systems administration.
• Static Typed Programming Languages: Experience in programming with Scala or Java.
• Agile/Scrum Methodology: Familiarity with working in agile/scrum-based project environments.
• Open-Source Contribution: Contributions to open-source projects are a strong plus.