Key Responsibilities

• AI System Design & Development – Architect and implement AI-driven solutions tailored to business and industry needs.
• Model Training & Optimization – Work with data scientists to train, fine-tune, and optimize AI models for performance and efficiency.
• AI Infrastructure Deployment – Set up and manage AI applications in cloud (AWS, GCP, Azure) or on-premise environments.
• Data Processing & Engineering – Develop data pipelines to preprocess, clean, and structure large datasets for AI/ML applications.
• Edge & Cloud AI Integration – Deploy AI models in real-time systems, IoT devices, and enterprise cloud environments.
• Automation & MLOps – Implement CI/CD pipelines, Kubernetes, Docker, and MLflow to streamline AI model deployment and lifecycle management.
• Security & Compliance – Ensure AI solutions adhere to ethical AI standards and industry regulations.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or a related field
• 3+ years of experience in AI/ML development and systems engineering
• Strong programming skills in Python, Java, or C++
• Expertise in AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Experience with Cloud AI services (AWS SageMaker, Azure ML, GCP AI Platform)
• Proficiency in DevOps & MLOps tools (Docker, Kubernetes, Jenkins, MLflow, Kubeflow)
• Understanding of data structures, algorithms, and distributed computing

Preferred Qualifications

• Certifications in AWS Certified Machine Learning, Google ML Engineer, or Microsoft Azure AI Engineer
• Experience with AutoML, Reinforcement Learning, or Generative AI
• Knowledge of Edge AI, AI chipsets (TPUs, NPUs), and IoT-based AI solutions

Job Benefits

• Competitive salary & performance-based incentives
• Work with the latest AI technologies and cloud platforms
• Remote or hybrid work options
• Career advancement opportunities in AI engineering and automation