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.