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Build and own the AI platform powering legal AI agents and accelerators, including orchestration, retrieval, evaluation, and inference at scale for document-heavy legal workflows.
Design and build scalable data pipelines and Lakehouse/Data Warehouse solutions using Microsoft Fabric, Azure Synapse, and Databricks to enable analytics and AI workloads.
Build and own a full-stack app with Angular UI, .NET APIs, SQL Server, and automated tests, while integrating agentic AI workflows using third-party LLMs and orchestration frameworks.
Designs AI-driven web apps by integrating LLMs, vector search, and real-time data; leads cloud-native architecture and full-stack development (Python + React) for production-ready solutions.
Build and maintain cloud-native AI infrastructure on Databricks and AWS, deploying ML models and LLMs at scale with MLOps pipelines and observability.
Build AI models for telecom networks: analyse telemetry, alarms, and topology to predict faults, optimize performance, and enable self-healing operations using Python, ML, and graph analytics.
Build and maintain AI-powered systems for competitive intelligence using LLMs, RAG, and agentic workflows to automate data collection and insights delivery across JFrog’s DevSecOps ecosystem.
Build and maintain Verkada’s enterprise data warehouse, automated pipelines, and analytics models to power company-wide reporting and AI-driven insights using Python, SQL, and cloud platforms like BigQuery or Snowflake.
Lead the architecture and long-term roadmap for enterprise-scale ML/AI and generative AI systems, defining platforms for building, deploying, and monitoring AI solutions while mentoring engineers and setting engineering standards.
Senior ML Engineer builds and maintains MLOps infrastructure to deploy AI/ML and generative AI systems into production, including APIs, vector databases, and RAG pipelines.
Build and maintain an AI-native data platform that transforms raw enterprise data into trusted, semantically meaningful assets using orchestration, governance, and AI-assisted development tools.
Build and maintain ML infrastructure to deploy and scale AI models in production, using Python, Kubernetes, and Ray. Own platform services that enable data scientists to move models from experimentation to live systems.
Design and lead AI-powered test automation frameworks using LLMs, RAG, and agentic AI to enhance software quality and integrate intelligent testing into CI/CD pipelines.
Build and deploy production-grade Generative AI services in Python, integrating LLMs, agents, and vector databases to power personalised travel experiences and smarter operations at Air New Zealand.
Build and maintain scalable .NET backend services and RESTful APIs using C#, SQL, and AWS, integrating cloud-native and AI components for enterprise applications.
Build and extend an offline, edge AI data-triage app in Python/Flask with local LLM models, vector stores, and a browser UI for document processing and search.
Design and implement scalable AI systems for manufacturing, integrating predictive analytics, LLMs, and MLOps to optimize production flows and decision-making.
Senior AI Engineer builds and integrates LLM-powered chatbots and NLP tools to deliver data-driven insights for automotive dealerships, using Python, TensorFlow/PyTorch, and cloud platforms.
Design autonomous network solutions for telecoms using AI frameworks (Vertex AI, LangGraph), APIs, and vector databases to optimize agentic systems and reduce inference costs.
Lead the AI and data strategy for a compliance SaaS platform, building production-grade generative AI, LLM systems, and MLOps pipelines in a regulated financial-tech environment.
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