Machine Learning Operations Specialist with Expertise in Generative AI
1 week ago
About Straive
We are a leading Content and Data Technology company providing innovative data services, subject matter expertise, and technology solutions to various domains.
Our mission is to empower businesses through cutting-edge analytics and AI capabilities. With a client base spanning 30 countries worldwide, we have strategically located our multi-geographical resource pool in eight countries - India, Philippines, USA, Nicaragua, Vietnam, United Kingdom, and Singapore.
Data Analytics & Al Services: Our Data Solutions business has become essential to our clients' success. We leverage technology and AI with human expertise-in-loop to create valuable data assets that our clients use to power their data products and workflows.
We are seeking an experienced Senior ML Ops / ML Ops with 3-7 years of experience in the Consumer Packaged Goods (CPG) sector, specializing in Machine Learning. The ideal candidate will possess a deep understanding of the machine learning lifecycle, along with the expertise to architect and implement scalable, resilient ML Ops solutions.
Key Responsibilities:
- Collaboration: Work closely with data scientists and ML engineers throughout the ML lifecycle, supporting model development, deployment, and monitoring.
- ML Ops Pipeline Development: Design, implement, and optimize ML Ops pipelines using tools and frameworks such as TensorFlow Serving, Kubeflow, MLflow, or similar technologies.
- Data Pipeline Engineering: Build and maintain data pipelines and infrastructure necessary for enterprise-scale machine learning applications, focusing on tasks like data ingestion, preprocessing, transformation, feature engineering, and model training.
- Cloud Implementation: Develop cloud-based ML Ops solutions on platforms such as Azure ML, AZURE SageMaker, or Google Cloud AI Platform.
- Azure Proficiency: Demonstrated experience with Azure cloud services, including Azure ML, Azure DevOps, AKS, Azure Container Registry (ACR), Azure Application Insights, and Azure Log Analytics.
- Containerization Skills: Familiarity with containerization technologies like Docker and Kubernetes.
- Model Deployment and Monitoring: Deploy and monitor various machine learning models in production, including text/NLP and generative AI models.
- CI/CD Automation: Build and maintain CI/CD pipelines using tools such as GitLab CI, GitHub Actions, or Airflow to streamline the ML lifecycle.
- Model Review and Quality Assurance: Participate in data science model reviews, focusing on code optimization, containerization, deployment, versioning, and quality monitoring.
- Support Data Model Development: Contribute to data model development with an emphasis on auditability, versioning, and data security, implementing practices like lineage tracking and model explainability.
- Mentorship: Provide guidance and support to junior engineers, fostering a collaborative and innovative team environment.
- Qualifications:
- Experience: Minimum of 3 -7 years of relevant work experience in ML Ops.
- Domain Knowledge: Strong expertise in Generative AI, advanced NLP, and machine learning techniques.
- Production Experience: Proven experience in deploying and maintaining production-grade AI solutions.
- Communication Skills: Excellent communication and teamwork skills, with the ability to work independently when needed.
- Problem-Solving: Strong analytical and problem-solving capabilities.
- Continuous Learning: Stay informed about the latest advancements in ML Ops technologies and actively explore new tools and techniques to improve system performance and reliability.
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