We  are building the next generation of AI-enabled industrial safety and operational intelligence platforms, combining IoT wearables, real-time communications, and intelligent automation to improve security, response times, workforce safety, and operational visibility across critical industries.

You will also contribute to the development of AI-enabled capabilities across  different  platform, helping transform real-time wearable and IoT data into actionable operational and safety intelligence. This includes working on intelligent event processing, predictive analytics, automation workflows, and AI-assisted decision-making systems for industrial and security environments.

Additional Key Responsibilities

· Design and implement AI-assisted backend services and intelligent automation workflows.

· Develop pipelines for processing real-time IoT and wearable telemetry data for analytics and event detection.

· Integrate AI/ML models into production-grade APIs and web applications.

· Build systems that support anomaly detection, predictive alerting, behavioral analysis, and operational intelligence.

· Work with structured and unstructured datasets to support AI-driven insights and reporting.

· Collaborate with product and engineering teams to develop AI-enabled safety and security applications.

· Optimize data architectures to support large-scale event streaming, model inference, and real-time decision systems.

· Assist in evaluating and integrating LLMs (Large Language Models) and AI tooling into internal and customer-facing products.

Additional Must-Have Qualifications

· Experience in integrating AI/ML capabilities into production applications.

· Familiarity with Python AI ecosystems such as TensorFlow, PyTorch, Scikit-learn, or similar frameworks.

· Understanding of data pipelines, event-driven architectures, and real-time data processing.

· Experience working with APIs for AI services and model inference.

· Familiarity with vector databases, embeddings, or semantic search concepts.

· Strong understanding of scalable backend architectures for data-intensive applications.

Additional nice-to-have-qualifications

· Experience with generative AI, LLM orchestration frameworks, or AI agents.

· Familiarity with edge AI or deploying lightweight AI models on IoT devices.

· Experience with streaming technologies such as Kafka, MQTT, or RabbitMQ.

· Knowledge of computer vision, geospatial analytics, or sensor fusion systems.

· Experience building AI-enabled dashboards, operational intelligence platforms, or safety monitoring systems.

· Exposure to MLOps workflows, model monitoring, and AI infrastructure deployment.

· Experience with Retrieval-Augmented Generation (RAG) architectures and private AI systems.

· Familiarity with AI security, privacy, and governance best practices for enterprise or government environments.

· Experience building or supporting mobile applications (iOS, Android, or cross-platform frameworks like React Native or Flutter).

· Exposure to DevOps practices and cloud platforms, especially AWS (e.g., EC2, S3, Lambda, RDS).

· Understanding of CI/CD pipelines and deployment automation.

· Familiarity with containerization tools such as Docker and Podman.