Bridge the gap between research and production by building scalable ML systems that power our AI Agentic platform. You'll implement machine learning pipelines, deploy AI models at scale, and ensure our Xyranik agents can learn and adapt in real-world environments.
Responsibilities
Build end-to-end machine learning pipelines for AI agent training
Deploy and scale ML models for autonomous AI agent systems
Design infrastructure for continuous learning and model adaptation
Optimize ML system performance for real-time AI agent operations
Collaborate with research team to productionize cutting-edge algorithms
Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or related field
3+ years of experience in machine learning engineering or MLOps
Robust programming skills in Python and ML frameworks
Experience with containerization, orchestration, and cloud computing
Understanding of ML system design and deployment best practices
Familiarity with data processing frameworks and model serving
Preferred Qualifications
Experience with AI agent systems and autonomous learning
Knowledge of feature stores and ML metadata management
Background in distributed training and large-scale ML systems
Experience with real-time ML inference and edge deployment
About FactoFlare
FactoFlare is a leading AI technology company focused on developing intelligent solutions that help businesses streamline operations, enhance productivity, and drive innovation. We are committed to creating a diverse, inclusive workplace where all employees can thrive and contribute to our mission of making advanced AI technology accessible and beneficial to organizations of all sizes.
We offer competitive compensation, comprehensive benefits, and a collaborative, flexible work environment that supports both professional growth and
📌 Machine Learning Engineer (Bengaluru)
🏢 FactoFlare AI
📍 Bengaluru
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