AI Developer

Requirements

Senior AI Engineer

backend engineering and AI system architecture

Key Responsibilities

• Architect and develop scalable AI systems, emphasizing backend services and operationalization
• Build end-to-end AI pipelines (data preprocessing, training, model serving, monitoring)
• Design and implement APIs, microservices, and backend logic for real-time AI model interaction
• Collaborate with DevOps and infrastructure teams to enable cloud, hybrid, and edge deployments
• Ensure quality, reproducibility, and maintainability via CI/CD, Docker, Kubernetes, and version control
• Optimize systems for performance and hardware acceleration (GPUs, Jetson, ONNX, TensorRT)
• Establish best practices in model governance, monitoring, and observability
• Mentor and guide junior engineers.
  

Must-Have Skills

• Strong programming skills in Python (bonus: Go, Rust)
• Experience in FastAPI, Flask, gRPC or similar frameworks
• Solid understanding of AI system architecture and lifecycle management
• Hands-on with Docker, Kubernetes, CI/CD pipelines
• Experience with distributed systems, asynchronous processing, API design
• Knowledge of ML lifecycle tools (MLflow, DVC, Weights & Biases)
• Strong Linux and containerization experience.
  

Nice to Have

• Exposure to computer vision frameworks (YOLO, UNet, transformers)
• Experience with streaming inference (Kafka, NVIDIA DeepStream)
• Familiarity with edge AI hardware (Jetson, Coral)
• Cloud deployment experience (AWS, GCP, Azure)
• Contributions to open-source or research in AI/ML systems.
  

Qualifications

• B.E./B.Tech/M.Tech in Computer Science, Software Engineering, or related field
• 5+ years of software engineering experience (preferably in AI/ML products)
• Proven track record of building production-grade AI systems