2 months ago
India (city not specified)Senior
Responsibilities
- Design, build, train, deploy, and monitor production machine learning models and AI services.
- Build end-to-end ML pipelines for data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
- Experiment with and rapidly prototype open-source ML models, including fine-tuning models for NLP and vision tasks.
- Generate synthetic data, validate models, and optimize large-scale transformer models for GPU performance.
- Review, refactor, optimize, containerize, deploy, version, and monitor data science models and code.
- Monitor, detect, and mitigate risks associated with LLMs and agentic systems.
- Implement observability for traces, logs, metrics, data and feature drift, model performance, safety signals, and cost.
- Develop templates, SDKs, CLIs, sandbox datasets, and documentation to streamline ML delivery.
- Design and develop Privacy APIs and backend infrastructure supporting large-scale data and privacy workflows.
- Optimize models and systems for performance, scalability, reliability, low latency, and high throughput.
- Contribute to test strategies and software delivery practices supporting high agility and zero downtime.
Requirements
- 8+ years of experience in machine learning.
- Proficiency in Go or Python.
- Hands-on experience designing, developing, and deploying production ML models and AI services.
- Experience building end-to-end ML pipelines and AI-driven software systems, particularly with NLP and named entity recognition.
- Hands-on experience with Python, NumPy, Pandas, and Scikit-learn.
- Experience with TensorFlow or PyTorch and modern language-model architectures.
- Understanding of data pipelines, feature stores, model lifecycle, algorithms, data structures, scalability, and distributed systems.
- Experience developing high-throughput, low-latency systems and writing testable software.
- Experience with continuous integration and test-driven development.
- Preferred experience with NLP, recommendation systems, computer vision, MLOps tools, Docker, Kubernetes, and AWS, GCP, or Azure.
- Privacy, authorization, and authentication engineering experience is a plus.
Benefits
- Work-from-home expense support.
- Excellent health insurance options.
- Generous paid time off.
- Flexible hours.
Tech Stack
Apache AirflowAWSAzureDockerGoGoogle Cloud PlatformKubernetesMLflowNumPyPandasPythonPyTorchscikit-learnTensorFlow
Categories
About Skyflow
Skyflow builds a data privacy and runtime AI data control platform for enterprises, securing sensitive data across applications, data stores, and AI models/agents. Its SaaS APIs provide capabilities like vaulting, tokenization, data governance, and data residency to enable safe AI deployment and compliance. Founded in 2019 and headquartered in Palo Alto, it serves Fortune 500s and SaaS providers in financial services, healthcare, retail, travel, and hospitality.