Java AI Jobs

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Design and develop the Agentic AI system including the core architecture and modules of UMOS platform's autonomous decision-making system. Develop agent systems integrating AI-based routing, dispatching, and predictive models into actual service operation logic. Implement software logic that reacts in real-time to dynamic changes in mobility and logistics operations to make optimal decisions. Enhance agent decision-making ability using knowledge graph-based information. Design and operate distributed systems to ensure the stability and scalability of the agent system. Build simulation and test environments to verify the complex logic and decision-making processes of the Agentic AI system. Develop tools to monitor and analyze the performance, behavior, and safety of the operating agent system.

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Design and develop scalable backend services operating in cloud environments; develop backend and algorithm features for AI-based functionalities such as price prediction, route optimization, and automation services; develop stable B2B API integration with external partners and corporate clients; analyze functional requirements and design API interface structures; continuously refactor for service performance monitoring and structural improvement; participate in code reviews and technical decision-making to enhance quality; ensure stability and high availability of services operated in cloud and container-based environments.

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Translating business requirements into requirements for AI/ML models. Preparing data to train and evaluate AI/ML/DL models. Building AI/ML/DL models by applying state-of-the-art algorithms, especially transformers, sometimes leveraging existing algorithms from academic or industrial research. Testing, evaluating AI/ML/DL models, benchmarking their quality, and publishing the models, data sets, and evaluations. Deploying models in production by containerizing them. Working with customers and internal employees to refine the quality of the models. Establishing continuous learning pipelines for models with online learning or transfer learning. Building and deploying containerized applications on cloud or on-premise environments.

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Design, build, and maintain production grade distributed and decentralized systems for secure AI deployment in government environments. Develop and integrate Rust based networking and data synchronization components with systems written in Go, Swift, Python, Java, and C++. Implement and optimize network protocols and routing mechanisms, including NAT traversal, VPNs, and multi protocol mesh topologies. Architect robust data synchronization frameworks using content addressable storage, Merkle trees, CRDTs, and erasure coding. Develop cross platform networking solutions for iOS, Android, and heterogeneous device environments. Lead performance tuning, debugging, and optimization of distributed software under resource constrained and intermittent connectivity conditions. Ensure security first design through threat modeling, secure key management, and integration of cryptographic primitives and protocols. Collaborate with MLOps, Infrastructure, and Security teams to deploy and integrate AI components in restricted or air gapped systems. Contribute to cross functional architectural design discussions and documentation for internal and external (public sector) stakeholders. Maintain rigorous testing, observability, and validation practices to ensure system reliability and compliance readiness.

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The Software Engineer, Cloud Deployments will develop integrations of Mistral’s AI products with major cloud providers, focusing on seamless, scalable, and automated deployment workflows. The role involves cross-functional collaboration, defining standards, and influencing cloud providers’ roadmaps while automating product deployment and monitoring.

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Guide the technical evaluation phase for customers as a presales solutions architect, working hands-on to deliver and integrate Databricks' platform for big data, analytics, and AI solutions. Work with customers and the sales team to provide architectural guidance, develop demo applications, reference architectures, and support adoption and optimization of Databricks within enterprise ecosystems.

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You will serve as a technical advisor during the sales process, guiding hands-on technical evaluation and working with product teams to meet customer requirements. Your role involves supporting customers to achieve data-driven outcomes with the Databricks platform, creating reference architectures, and integrating Databricks with third-party applications.

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Guide the technical evaluation phase in a hands-on environment as a pre-sales solutions architect, serving as a technical advisor to the sales team. Assist customers with deriving concrete data-driven outcomes using the Databricks platform, supporting integration into enterprise ecosystems, optimizing deployments, and leading technical community interactions.