AI Solution Development

• Design, develop, and deploy AI-powered capabilities across the Device Management Platform
• Build intelligent search experiences leveraging LLMs, semantic search, vector databases, and retrieval-augmented generation (RAG)
• Build and maintain MCP tools and integrations for device management, compliance, ITSM, etc.
• Develop AI-driven insights using device telemetry, operational metrics, and platform data
• Implement conversational experiences and AI assistants that simplify device management workflows
• Build AI features for compliance evaluation, anomaly detection, natural-language admin commands, and self-healing using language models and local RAG.
• Implement the confidence gateway that converts probabilistic AI decisions into deterministic actions with policy thresholds, guardrails, audit logs, and human-approval paths.

AI Platform Engineering & Systems Integration

• Develop scalable services and APIs that integrate AI capabilities into existing platform workflows
• Build data pipelines and retrieval systems to support AI applications
• Evaluate and integrate foundation models, LLMs, embeddings, and agent frameworks
• Design and implement prompt engineering, evaluation, and observability frameworks
• Ensure AI solutions are secure, reliable, scalable, and cost-effective
• Integrate AI capabilities with device management services, backend platforms, and enterprise systems
• Collaborate with platform teams to operationalize AI solutions in production environments
• Build and maintain CI/CD pipelines supporting AI applications and services
• Partner with infrastructure teams to optimize AI workloads and deployments

Architecture & Technical Leadership

• Identify opportunities to leverage AI to improve operational efficiency and user experience
• Establish best practices for AI solution design, model evaluation, governance, and responsible AI usage
• Contribute to architectural decisions around scalability, reliability, security, and AI adoption

Collaboration & Cross-Functional Impact

• Partner with product, engineering, security, infrastructure, and business teams to identify AI use cases and prioritize investments
• Translate business problems into AI-powered solutions
• Communicate technical concepts and AI recommendations to both technical and non-technical stakeholders
• Stay current with emerging AI technologies and bring innovative ideas into the platform

Required Qualifications

• 4+ years of software engineering experience, including experience building production-grade applications and services
• Hands-on experience developing and deploying AI-powered applications or Generative AI solutions
• Experience building applications using Large Language Models (LLMs) such as OpenAI GPT, Claude, Gemini, Llama, or similar models
• Experience with Retrieval-Augmented Generation (RAG), vector databases, embeddings, and semantic search
• Understanding of model confidence, evaluation metrics, false positives/negatives, and safe fallback behaviour.
• Familiarity with AI governance, explainability, and approval-gated automation for high-risk actions.
• Exposure to Anomaly detection, Confidence scoring and decision thresholds, Explainability and Auditability
• Strong proficiency in Python, Java, Kotlin, or a combination thereof
• Experience designing and implementing REST APIs and backend service integrations
• Experience working with cloud-based AI services and AI development frameworks
• Experience evaluating AI solutions and measuring quality through testing, experimentation, and monitoring
• Strong understanding of software engineering best practices, CI/CD, testing, and observability
  • Strong problem-solving skills and ability to work across cross-functional teams