5 months ago
Chennai, IndiaSenior
Responsibilities
- Architect and deliver end-to-end ML and reinforcement learning platforms, including pipelines, training and evaluation environments, model serving, monitoring, and incident response.
- Establish data quality, observability, governance, reproducible experimentation, and technical standards across ML products.
- Optimize production workflows for performance, latency, scalability, and security.
- Lead technical reviews, improve code and design quality, and drive initiatives from problem framing through production operations.
- Partner cross-functionally to define model objectives, success metrics, and deployment strategies aligned with business goals.
- Mentor and coach engineers and data scientists while promoting engineering best practices and continuous improvement.
Requirements
- Bachelor’s, master’s, or PhD in computer science or a related field.
- 5–8+ years of experience building and operating machine learning systems in production.
- Advanced proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Experience building enterprise applications with Java and Spring Boot.
- Expert-level experience with scikit-learn, pandas, NumPy, and machine learning model development and evaluation.
- Experience deploying systems on Azure, AWS, or GCP, with strong Docker and Kubernetes experience.
- Hands-on experience with monitoring, drift detection, alerting, and incident response for ML services.
- Preferred: simulation expertise, including discrete-event or agent-based simulation, queueing or stochastic modeling, and reinforcement learning in real-world settings.
- Preferred: MLOps experience with model versioning, experiment tracking, automated retraining, and governance.
- Preferred: logistics or supply-chain experience, optimization in complex or partially observable environments, and serverless or event-driven architectures using Knative, Azure Functions, or AWS Lambda.
Benefits
- Competitive salary and benefits.
- Inclusive culture and commitment to diversity.
- Opportunities to grow scope and leadership.
- Ownership of strategic ML initiatives with direct customer impact and influence over product direction and technical standards.
Tech Stack
AWSAzureDockerGoogle Cloud PlatformJavaKubernetesNumPyPandasPythonPyTorchscikit-learnSpring BootTensorFlow
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