MLOps Engineer

About the Role and Responsibilities

We're looking for engineers who have hands-on experience in building scalable and high-performance Machine Learning systems. As an MLOps Engineer, you will work on/contribute to design, implementation, maintenance & monitoring of real time and batch ML pipelines.

Your key responsibilities will be to

● Create reproducible pipelines to deploy ML models in production

● Design and create automated workflows for model training

● Collaborate with/mentor Data Scientists such that they can adopt certain engineering best practises in their workflows

● Monitor models in production

● Set up Inference systems (APIs) that support sub-second latencies and can scale to millions of users Key Qualifications

● 1.5+ years of hands-on experience in building Machine Learning pipelines or a similar DevOps role

● Strong Programming skills with proficiency in Python is mandatory

● Hands-on experience in building CI/CD Pipelines is mandatory

● Experience in containerization and orchestration (such as Docker, Kubernetes) is mandatory

● Hands-on experience in at least one of the following Cloud Computing Platforms (AWS, Azure, Google Cloud) is mandatory

● Experience in monitoring & observability tools such as Splunk (SignalFx), Prometheus, Grafana is a plus

● Understanding of deep learning workflows and experience with either PyTorch or TensorFlow is a plus