Machine Learning Engineer
Design, develop, and deploy Machine Learning models and ML pipelines for a healthcare client. Focus on optimizing models for document classification, segmentation, and data extraction in production environments.
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Design, develop, and deploy Machine Learning models and ML pipelines for a healthcare client. Focus on optimizing models for document classification, segmentation, and data extraction in production environments.
The engineer will collaborate with the Head of AI and product teams to develop and deploy AI-driven security features, focusing on designing, training, and integrating machine learning models for detecting malicious traffic and bots.
The role involves developing and maintaining the ML training platform and the bidding infrastructure used to evaluate ML models in production. Responsibilities also include identifying performance bottlenecks, optimizing critical system parts, ensuring scalability, and creating performance tests for new components.
You will develop and refine industry-leading NLP models and conversational AI agents like Lyro using advanced RAG techniques. The role involves building agent orchestration systems and optimizing data access for LLMs to improve customer service automation.
Design and implement end-to-end document intelligence pipelines on AWS while developing and optimizing ML models for document classification and extraction. You will own features from research through production deployment and establish quality metrics for extraction accuracy.
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Develop and maintain AI-assisted pipelines for automated documentation generation using self-hosted LLMs and RAG architectures. Collaborate with cross-functional teams to integrate vector search, graph queries, and automated validation scripts to ensure high-quality output.
You will own the AI/ML core of the robotics platform, designing neural network architectures and managing the end-to-end lifecycle from data processing to edge deployment. Additionally, you will collaborate with the CTO to define the technical strategy and mentor future engineers as the team scales.
Research and validate machine learning approaches for vessel detection using Distributed Acoustic Sensing (DAS) and AIS data. Develop scalable AI pipelines and proof-of-concept systems on Google Cloud to monitor subsea infrastructure.
Lead the design and deployment of end-to-end AI lifecycles and scalable computer vision pipelines for healthcare clients using the Databricks Lakehouse. Act as a strategic advisor on clinical validation, regulatory compliance, and MLOps architecture.
Design and ship reliable, scalable ML subsystems for a proactive AI assistant focusing on long-horizon workflows. Own the end-to-end lifecycle from data preparation and training to production inference and iteration.
Develop document-processing pipelines and autonomous AI agents using LLMs and agentic workflows. Build and optimize Python microservices for document ingestion, classification, and extraction while ensuring system reliability and cost-efficiency.
Participate in research projects, interviews, and surveys to provide expert insights on workflow management tools. Share professional expertise to help researchers and organizations improve AI models and development processes.
The role involves adhering to software development best practices and collaborating with senior machine learning teams. You will support project leaders in machine learning initiatives and engage in analytical processes related to AI solutions.
Design and implement retrieval and ranking architectures for personalized music recommendations. Build end-to-end ML systems encompassing data processing, training, deployment, and performance monitoring.
Architect and build agentic workflows and conversational systems using LLMs and reasoning components to optimize recruiting. Lead the end-to-end development of ML/NLP products from ideation through production and deployment.
You will architect and develop agentic workflows that integrate large language models and reasoning components to create adaptive conversational systems. Additionally, you will lead the end-to-end development of ML/NLP products, from ideation and model training to production deployment.
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