Full-Stack Developer (Ai)

1) AI System Development (Core Focus)

• Design and deploy end-to-end AI systems (data flow → model integration → output control → monitoring)
• Work with AI APIs (e.g., OpenAI or equivalent platforms) and self-hosted models
• Fine-tune or optimize models for business-specific use cases
• Build structured prompt frameworks and enforce schema-based output control
• Implement validation layers, retry logic, logging, and AI quality control mechanisms
• Optimize latency, inference cost, and production stability
• Establish monitoring and observability for AI performance

2) Backend & AI Architecture

• Develop RESTful APIs to serve AI services
• Design microservice-based architecture for modular AI systems
• Implement authentication and authorization (JWT, OAuth 2.0, RBAC)
• Build dashboards for AI usage monitoring, analytics, and reporting
• Manage model versioning and environment separation (dev/staging/prod)
• Ensure scalability, security, and maintainability of AI services

3) Data & Model Handling

• Process, clean, and structure real-world datasets
• Build data pipelines for training and inference
• Implement model evaluation and performance monitoring systems
• Design feedback loops for continuous model improvement

4) IoT Integration (Supporting Layer)

• Integrate data from IoT devices (ESP32, sensors, embedded systems)
• Design communication using MQTT / HTTP / WebSocket
• Standardize and preprocess device data before AI ingestion
• Ensure secure device-to-server communication
• Maintain reliable real-time data streaming pipelines

• 1.5–3+ years of hands-on experience working with AI/LLM systems
• Proven experience deploying AI systems into production
• Experience fine-tuning or customizing models for business use cases
• Strong proficiency in Python and backend development
• Experience integrating AI APIs (e.g., OpenAI) or self-hosted LLMs
• Basic understanding of IoT data pipelines and device communication
• Strong system design mindset with a production-first approach
• Security-aware engineering practices

Preferred Qualifications

• Experience with containerization (Docker, Kubernetes)
• Familiarity with vector databases and embedding pipelines
• Experience with cloud platforms (AWS, GCP, Azure)
• Knowledge of monitoring tools (Prometheus, Grafana)
• Experience optimizing LLM latency and cost at scale

• Competitive salary with performance-based bonuses
• Health and life insurance
• Opportunities for career advancement and skill development
• Paid time off and public holidays
• Access to cutting-edge AI tools and technologies