Learning Engineer
1 month ago
Job Role - Lead ML Ops
Experience - 7+ years
Location - Bangalore/Hyderabad
Mode - Hybrid (3 days WFO)
About Straive:
Straive is a market leading Content and Data Technology company providing data services, subject matter expertise, & technology solutions to multiple domains.
Data Analytics & Al Solutions, Data Al Powered Operations and Education & Learning form the core pillars of the company’s long-term vision. The company is a specialized solutions provider to business information providers in finance, insurance, legal, real estate, life sciences and logistics. Straive continues to be the leading content services provider to research and education publishers.
Data Analytics & Al Services: Our Data Solutions business has become critical to our client‘s success. We use technology and Al with human experts-in loop to create data assets that our clients use to power their data products and their end customers‘ workflows. As our clients expect us to become their future-fit Analytics and Al partner, they look to us for help in building data analytics and Al enterprise capabilities for them.
With a client-base scoping 30 countries worldwide, Straive’s multi-geographical resource pool is strategically located in eight countries - India, Philippines, USA, Nicaragua, Vietnam, United Kingdom, and the company headquarters in Singapore.
Key Responsibilities:
Collaborate with data scientists and ML engineers throughout the ML lifecycle, from model development to production deployment.
Design and implement MLOps pipelines using tools and frameworks like TensorFlow Serving, Kubeflow, MLflow, or similar solutions.
Design, develop, and implement data pipelines and engineering infrastructure to support enterprise-scale machine learning systems. This could involve tasks such as data ingestion, pre-processing, transformation, feature engineering, and model training.
Design and implement cloud-based MLOps solutions using platforms like Azure ML, Azure Databricks, AWS SageMaker, or Google Cloud AI Platform.
Hands-on experience with Azure cloud services, including Azure ML, Azure DevOps, AKS, Azure Container Registry (ACR), Azure Application Insights, and Azure Log Analytics, is essential.
Experience in containerization technologies like Docker and Kubernetes is a plus.
Experience deploying and maintaining various types of machine learning models in production, including text/NLP models, is required.
Experience with Generative AI (GenAI) projects is a plus.
Build CI/CD pipelines using tools like GitLab CI, GitHub Actions, Airflow, or similar solutions to automate the ML lifecycle.
Perform data science model reviews, including code refactoring and optimization, containerization, deployment, versioning, and monitoring of the model‘s quality.
Support data model development, with a focus on auditability, versioning, and data security. This could involve implementing practices like lineage tracking, model explainability, and bias detection.
Mentoring junior MLOps engineers or collaborating with multiple teams like consulting, data engineering, development team.
Qualifications:
Total 7+ years of work experience , 3+ years in MLOps
Strong expertise in GenAI, advanced NLP, computer vision, and ML techniques.
Experience in designing, developing, and deploying production-grade AI solutions.
Excellent communication and collaboration skills. Ability to work independently and as part of a team.
Strong problem-solving and analytical skills.
Stay up-to-date on the latest advancements in MLOps technologies and identify and evaluate new tools and techniques to improve the performance, maintainability, and reliability of machine learning systems.
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