Senior machine learning engineer

2 weeks ago


Bangalore, India Grizmo Labs Full time

Responsibilities ML Platform End-to-End Development: Architecture and Best Practices Data Structurization: Structure information from multiple data sources, including documents, websites, and emails Agentic Systems: Develop agentic systems using the latest frameworks, such as Lang Graph, to automate various operational activities MLOps: Implement end-to-end machine learning pipelines, including data preprocessing, model training, validation, deployment, A/B testing, and regression testing Other Responsibilities: Tech Qualifications: Engineers who understand the underlying principles and demonstrate quick learning abilities, along with adaptability to evolving tech stacks, are highly valued. While the following tech stack experience is preferred, specific experience in any one technology is not prioritized over another Experimentation: Design and run experiments with foundational LLMs, including Retrieval-Augmented Generation (RAG), fine-tuning, new cognitive architectures, and retrieval techniques Scalability: Ensure that ML systems are scalable and optimized for low latency and high performance Accuracy: Minimize hallucinations in the model outputs and aim to make them more deterministic Adaptability: Architect highly adaptable ML systems given the fast-evolving nature of AI technologies Cross-Functional Collaboration: Collaborate with backend developers, frontend developers, UX/UI designers, and founders to ensure cohesive integration of ML systems Stay Up-to-Date: Stay current with the latest developments in ML and LLM technologies to evaluate their applicability Documentation: Create and maintain comprehensive documentation for ML models and their applications Qualifications Programming Languages: Python or any other relevant programming language Machine Learning Frameworks: Tensor Flow, Py Torch Cloud Platforms: GCP, Kubernetes, Cloud Run Data Structurization Technologies: OCR technologies MLOps Tools: MLflow, Kubeflow Vector Databases: Experience with vector databases APIs: REST, Graph QL Testing: Pytest, unit testing frameworks Observability: ELK, Prometheus/Grafana Data Isolation and Security: Integrate with Google Cloud Identify to ensure multi tenancy Preferred Qualifications Experience building machine learning pipelines to structure data using deep neural network platforms such as Document AI or LLMs for specific operational tasks Experience building ML pipelines that automate operational tasks Experience with the latest LLM techniques including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG)



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