Build and deploy scalable MLOps platforms and Generative AI solutions on GCP to enable seamless collaboration among data scientists, ML engineers, and business teams.
Work closely with Product Owners, Tech Anchors, and Data Scientists to design and implement solutions using Python and modern ML/AI tooling.
Maintain CI/CD pipelines to support continuous integration, testing, and deployment of ML models and cloud-native applications.
Enhance automation within DevOps ecosystems, strategically identifying bottlenecks and implementing improvements for faster, more reliable releases.
Support ML workflows using orchestration tools like Airflow and KFP, ensuring streamlined experimentation and deployment pipelines.
Develop and operationalize Gen AI applications, including Retrieval-Augmented Generation (RAG) and Multi-Agent Systems leveraging LLMs.
Ensure code quality and system reliability by performing regular code reviews, debugging, and resolving complex issues across data pipelines and microservices.
Mentor peers through paired programming, enabling a collaborative culture for cross-training, rapid problem-solving, and innovation.
Utilize cloud-native tools and services such as Vertex AI, BigQuery, and Cloud Functions to deliver robust, cost-efficient AI/ML infrastructure.
Implement infrastructure as code (IaC) using tools like Terraform and manage containerized environments with Docker and Kubernetes.
Bachelor's degree in Computer Science or a related technical discipline with a strong foundation in software engineering and systems design.
India
USA
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