AI Engineer

Key Responsibilities Build data-driven web applications including micro-frontends, dashboards, interactive visualizations, alerts, KPIs and reporting views. Develop scalable APIs and backend services to process and expose telemetry, analytics, alerts and AI/ML outputs from system likes ThingsBoard, time-series databases and data lakes. Turn complex telemetry and model outputs into clear insights through meaningful charts, trends, aggregations, summaries and reports. Design production-grade services using service-oriented/microservices and event-driven architecture, with appropriate asynchronous processing and integration patterns. Ensure production readiness through scalability, multi-tenancy, observability, secure coding, performance optimization, fault handling and monitoring. Integrate with ML/AI, OT and platform services and iterate rapidly with pilot customers to deliver and refine the MVP. Technical Skills Mandatory Frontend, Dashboarding & Visualization Strong modern frontend development using React, Angular, Vue or equivalent. Hands-on experience building dashboards, analytics applications and reporting interfaces using real-world data. Experience with interactive data visualization , including charts, trends, KPIs, alerts and time-series views. Working knowledge of at least one visualization/dashboarding technology such as ECharts, Plotly, D3.js, Grafana, Kibana or equivalent . Backend & APIs Strong backend/API development using Node.js, Python, Java or equivalent. Hands-on experience with REST APIs, JSON, authentication, authorization and service integration . Understanding of service-oriented/microservices architecture and scalable API design. Understanding of event-driven and asynchronous processing concepts. Data Working knowledge of SQL and data modelling . Practical familiarity with NoSQL and/or time-series databases . Experience handling large, dynamic or time-series data , including querying, aggregation and transformation. Cloud, DevOps & Production Hands-on Docker/containerization and CI/CD experience. Understanding of cloud deployment, scalability, performance and production troubleshooting . Practical familiarity with application observability , including logs, metrics and monitoring. Ability to use dashboards, logs and monitoring data to troubleshoot production issues . Security & multi-tenancy Secure coding and API security fundamentals, including authentication, authorization, input validation and secrets management. Understanding of multi-tenant application concepts , including tenant-aware access and data isolation. Preferred Experience with ThingsBoard or similar IoT/telemetry platforms . Experience with Grafana, Kibana, ELK/Elastic Stack, Loki, Prometheus, OpenTelemetry, Jaeger, Tempo, Dynatrace, AppDynamics, CloudWatch or equivalent . Experience integrating applications with log aggregation, monitoring, APM and distributed tracing platforms . Familiarity with Kafka, MQTT or equivalent messaging/event platforms . Experience with time-series databases such as TimescaleDB, InfluxDB or equivalent. Experience with data lakes and high-volume telemetry platforms . Familiarity with RAG, LLM APIs and Agentic AI . Experience with Kubernetes and cloud-native architectures . Domain exposure to Energy, Industrial Power or OT systems . Qualifications Mandatory 4+ years of professional full-stack development experience building and deploying production applications. Strong hands-on capability across frontend, backend and database layers , with the ability to work independently across the stack. Demonstrated experience building data-driven dashboards, visualization, analytics or reporting applications . Experience developing production-grade, scalable and secure applications . Practical experience with cloud environments, Docker and CI/CD . Strong production troubleshooting and problem-solving capability , including the ability to diagnose issues across application, API, data and infrastructure layers. Ability to work in a fast-moving MVP environment , prototype quickly and incorporate customer/pilot feedback. Strong communication and collaboration skills for working with ML/AI, data, OT and platform engineering teams . Preferred Experience building products for telemetry, IoT, analytics, monitoring or operational use cases . Experience integrating with observability and monitoring ecosystems , rather than only implementing application-level logging. Experience with multi-tenant SaaS platforms at production scale. Experience with event-driven/distributed systems and high-volume data applications. Experience integrating AI/ML model outputs or GenAI capabilities into production applications. Energy, Industrial Power or OT domain experience is an advantage, but not mandatory .