Staff Software Engineer - Python, Computer Vision
On-siteBengaluru, Karnataka, India
Job Summary
Design and lead the development of core machine learning platform components for video ingestion, preprocessing, annotation, training, and serving on Google Cloud Platform. Build APIs, microservices, and cloud-native AI systems using Terraform, Kubernetes, and Vertex AI to enable scalable model deployment and observability. Evaluate, fine-tune, and deploy open-source Computer Vision and Multimodal AI models for production use cases while collaborating with cross-functional teams to establish engineering excellence. Mentor engineers and influence architecture across teams to set long-term technical direction for Video AI platform capabilities.
Required Qualifications
- 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 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
Desired Qualifications
- 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
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