Sr.AI Developer

Senior AI Developer

This role involves working on system architecture, data pipelines, APIs, model serving, and monitoring frameworks. You will collaborate with cross-functional teams to ensure scalable, robust, and efficient AI deployments, while also mentoring junior engineers and promoting best practices in engineering excellence.

Key Responsibilities

• Design and lead the development of scalable AI architectures with a focus on backend robustness and system orchestration.
  
• Build and maintain modular AI pipelines, including data preprocessing, training, model serving, and monitoring.
  
• Develop APIs, microservices, and backend logic to enable seamless AI model integration and real-time inference.
  
• Collaborate with DevOps and infrastructure teams to deploy AI solutions across cloud, hybrid, and edge environments.
  
• Ensure code quality and maintainability through CI/CD pipelines, version control, and containerization.
  
• Optimize system performance, including profiling, parallelization, and GPU/edge deployment strategies.
  
• Implement best practices for model reproducibility, observability, and governance using tools like MLflow, Prometheus, or Grafana.
  
• Mentor junior team members in scalable AI system design and backend engineering practices.
  

Must-Have Skills

• Strong programming skills in

  Python

  (Go or Rust experience is a plus).
• Proficiency in backend frameworks such as

  FastAPI, Flask, or gRPC

  .
• Solid understanding of AI system architecture and lifecycle management.
  
• Experience with

  Docker, Kubernetes, and CI/CD

  for production ML deployments.
• Knowledge of distributed systems, asynchronous processing, and real-time API design.
  
• Familiarity with

  MLflow, DVC, or Weights & Biases

  for model tracking/versioning.
• Hands-on experience with Linux environments and containerized AI workflows.
  

Good to Have

• Experience in computer vision (e.g., YOLO, UNet, transformers).
  
• Knowledge of streaming inference systems like

  DeepStream or Kafka

  .
• Exposure to edge AI hardware (Jetson, Coral) and inference optimization (ONNX, TensorRT).
  
• Familiarity with cloud platforms (AWS, GCP, Azure).
  
• Experience with data augmentation, synthetic data, or simulation tools.
  
• Contributions to open-source or research publications in AI/ML infrastructure.
  

Qualifications

• B.E./B.Tech/M.Tech in Computer Science, Software Engineering, or related field.
  
• 5+ years of professional experience in software engineering, ideally within AI/ML product companies.
  
• Proven track record in designing and deploying production-grade AI systems.