Staff Software Engineer - Python, Computer Vision

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Staff Software Engineer - Video AI Team, Bangalore

• Design and lead the development of core components of the machine learning platform with a focus on scalability, reliability, and cloud-agnostic principles.
• Build APIs and microservices that power end-to-end machine learning workflows, including model deployment, orchestration, model registry integration, and observability.
• Architect and deploy AI/ML pipelines for video ingestion, preprocessing, annotation, training, evaluation, serving, monitoring, and continuous improvement.
• Develop cloud-native AI systems on Google Cloud Platform using Vertex AI, Vertex AI Pipelines, Vertex AI Endpoints, GKE, Cloud Run, BigQuery, Cloud Storage, Pub/Sub, and related services.
• Own cloud infrastructure and CI/CD automation using Infrastructure-as-Code principles, with hands-on implementation using Terraform, Kubernetes, Docker, and deployment automation tools.
• Evaluate, fine-tune, train, optimize, and deploy open-source Computer Vision and Multimodal AI models and Vision Transformers for production use cases.
• Design and operationalize Deep Learning, Computer Vision, Generative AI, LLM, VLM, and RAG-based capabilities for video understanding and content intelligence workflows.
• Collaborate with content protection and security teams to ensure robust access controls, secure model/data access patterns, and compliance-aligned platform practices.
• Champion engineering excellence through well-tested code, clear documentation, design reviews, code reviews, and continuous improvement of development and deployment practices.
• Establish and enforce best practices in testing, code quality, monitoring, observability, incident readiness, and reliability for ML pipeline components.
• Work closely with AI engineers, data scientists, product managers, and platform teams to support evolving use cases such as scene segmentation, video annotation, clip generation, metadata enrichment, recommendations, and media workflows.
• Mentor engineers, influence architecture across teams, and help set long-term technical direction for Video AI platform capabilities.

• 9-12+ years of software engineering experience building scalable backend systems, distributed services, and cloud-native platforms.
• Deep hands-on experience with cloud platforms such as GCP, with the ability to design cloud-agnostic deployment patterns.
• Strong hands-on experience with GCP and Vertex AI platforms, including Vertex AI Pipelines, Model Registry, Endpoints, GKE, Cloud Run, BigQuery, Cloud Storage, Pub/Sub, Cloud Monitoring, and Cloud Logging.
• Experience working with production deployment of machine learning systems, including model training, evaluation, serving, monitoring, rollback, and lifecycle management.
• Strong knowledge of Deep Learning, Computer Vision, and Generative AI models, including practical experience with modern ML frameworks such as PyTorch, TensorFlow, and Hugging Face ecosystem.
• Hands-on experience evaluating, fine-tuning, training, and deploying open-source Computer Vision and Multimodal AI models for production use cases, including model optimization, transfer learning, distributed training, and performance benchmarking.
• Excellent proficiency in Python with strong software design, API design, and architecture skills.
• Strong experience with Terraform, Kubernetes, Docker, containerized deployments, CI/CD workflows, and Infrastructure-as-Code practices.
• Experience building APIs, microservices, event-driven systems, and asynchronous processing patterns for large-scale ML workflows.
• Experience in building and maintaining observability stacks such as Prometheus, Grafana, distributed tracing, and cloud-native monitoring tools.
• Familiarity with model serving technologies such as Triton, TorchServe, vLLM or similar platforms is preferred.
• Experience with RAG architectures, LLM/VLM deployment, vector databases, prompt engineering, and GPU-based inference systems is preferred.
• Familiarity with Agile development practices, strong operational ownership, and a mindset for reliability, scalability, maintainability, and cost optimization.
• Experience collaborating in cross-functional teams and setting engineering standards at scale through technical leadership, mentorship, and documentation.
• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Engineering, or a related field; equivalent practical experience will also be considered.

• A Great Place to work.
• Equal opportunity employer
• Fast track growth opportunities