Key Qualifications

- 5+ years of software, cloud, DevOps, ML engineering, or related engineering experience, with meaningful hands-on experience supporting ML/AI workloads in production.
- Demonstrable hands-on experience with the Google Cloud AI/ML stack, particularly production deployment and operationalization of ML workloads.
- Strong Python development and automation skills.
- Practical experience building and maintaining CI/CD pipelines for ML or AI applications.
- Hands-on knowledge of containerization and orchestration using Docker and Kubernetes/GKE.
- Experience with model deployment, versioning, monitoring, rollback, and lifecycle management.
- Valuable knowledge of Vertex AI, or comparable managed ML platforms.
- Understanding of machine learning workflows covering data preparation, training, evaluation, deployment, monitoring, and retraining.




- Familiarity with Generative AI concepts such as LLMs, embeddings, RAG, vector databases, prompt-based applications, and agentic AI.
- Understanding of production monitoring concepts including service health, model performance, drift, latency, resource utilization, and failure monitoring.
- Familiarity with Infrastructure as Code, preferably Terraform.
- Experience troubleshooting distributed cloud applications and production ML pipelines.
- Comfortable working in an agile sprint environment with defined engineering tickets, code reviews, and release cycles.
- Basic understanding of responsible AI, security, and data privacy, with the ability to identify and escalate potential concerns.

📌 Mlops Engineer (Bengaluru)
🏢 SRS Infoway
📍 Bengaluru