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we are looking for a AI Applied Scientist - Insights & Recommendations.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team , you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.

You will join the Deployment Platform team, which puts technology directly in the hands of customers, field technicians, and partners. As an AI-Native mobile engineer, your primary development framework is Flutter (with native iOS and Android used when needed), building the mobile application central to field deployment. You are also comfortable across the full stack to ensure end-to-end feature delivery. Most importantly, you will shape the next generation of agentic installation experiences that define how industrial AI reaches the factory floor. This is a high-impact role at the intersection of mobile, IoT, and AI.
A Day In Your Life:
Design, build, and maintain high-quality, cross-platform mobile applications for iOS and Android using the core Flutter/Dart stack.
Lead the design and shipment of agentic-first workflows for guided installation, leveraging LLMs (Azure OpenAI, Gemini) to assist technicians and automate decision-making in the field.
Implement complex IoT/Hardware integration features, including NFC provisioning, AR field tools, and indoor positioning/navigation.
Contribute to backend and frontend services/APIs to ensure seamless, end-to-end feature delivery.
Architect and maintain a scalable, modular monorepo, ensuring robustness through strong state management (BLoC/RxDart) and comprehensive test coverage.
Collaborate with product and design teams to deliver exceptional UI/UX, including adaptive and RTL-aware layouts.

we are looking for a Algorithm and Applied AI Scientist (DSP).
As an Algorithm and Applied AI Scientist (DSP), you will serve as the technical lead for an innovative project focused on advanced signal processing and data analytics applications. You will work with unique datasets from various signal-generating sources, researching and developing novel models, and analysing, maturing, and extracting features that have a significant impact on industrial manufacturers globally. In this role, you will be instrumental in defining project objectives, identifying customer value, and pushing the team toward successful outcomes. Joining AI team offers ongoing professional growth in leadership, algorithm development, research, innovations, and technical data analysis.
A Day In Your Life:
Be the technical lead of an innovative project.
Develop a comprehensive understanding of industrial data by exploring its physical properties and the relationships among different data streams.
Oversee the full algorithm development cycle, spanning from initial problem definition to the deployment of production-ready feature-based AI models, SDLC starting from feature discovery that is being tracked by sensor behavior patterns.
Architect and build diverse predictive and anomaly classification models using sensor time-series and textual information.
You will gain a deep proficiency in the industrial world and the connection between datasets to generate actionable insights.
Utilize classic and cutting-edge technologies, such as Generative AI, transformers, TSFM, TGNN, deep learning, and traditional statistical methods, time-series, and textual data to generate actionable insights.
Partner with the product team and external customers to create data-driven solutions that address real-world business needs.

we are looking for a Experienced AI Software Engineer.
You are a Senior AI Software Engineer looking to join a GenAI Infrastructure team and build production-grade GenAI capabilities and the infrastructure behind them. You will be a hands-on engineer focused on AI agents, RAG pipelines, tool-calling workflows, backend services, data access layers, evaluation, observability, and reusable GenAI infrastructure used across product teams. You will not be focused solely on model research or prompt design. Instead, you will be a strong software engineer who can design, build, ship, and operate reliable AI-powered systems in production.
A Day In Your Life :
Build reusable infrastructure, SDKs, and internal frameworks for AI-powered product capabilities.
Design and implement AI agents, RAG flows, tool-calling workflows, and LLM orchestration pipelines.
Build production-grade GenAI services, APIs, and backend infrastructure.
Integrate LLM workflows with internal microservices, data platforms, vector search, and event-driven systems.
Design secure data-access patterns that enforce authorization, tenant separation, and user-level scope.
Implement evaluation, tracing, monitoring, and quality-control mechanisms for GenAI systems.
Improve latency, reliability, fallback behavior, cost efficiency, and production readiness.
Work on customer-facing AI experiences, including conversational and proactive agentic product flows.
Collaborate with backend engineers, product managers, data engineers, AI/ML engineers, and domain experts.