AI Engineer

Core Responsibilities of an AI Engineer

Must have

• Integrate CI/CD pipelines
• Manage containers (e. g. , Docker, Kubernetes)
• Automate deployment and scaling processes
• Model Development & Deployment: Build scalable AI/ML models for real-world applications, including NLP, computer vision, and GenAI use cases123.
• Data Engineering: Preprocess large-scale structured and unstructured datasets, optimize pipelines, and extract insights7.
• MLOps & Automation: Implement best practices for training, deployment, and monitoring of AI systems using tools like MLflow, Docker, and cloud platforms (AWS, Azure, GCP)4.

Good to have

• Collaboration: Work with cross-functional teams software engineers, data scientists, product managers to integrate AI into products1.
• Research & Innovation: Stay updated with the latest AI trends, research papers, and tools (e. g. , LangChain, RAG, LLMs)5.
• Mentorship: Guide junior engineers and contribute to team development3.

Skills & Qualifications

• Programming: Strong in Python, with experience in TensorFlow, PyTorch, Scikit-learn, and data manipulation libraries (Pandas, NumPy)
• AI Concepts: Deep understanding of supervised/unsupervised learning, NLP, deep learning, and GenAI.
• Tools & Frameworks: Experience with vector databases, containerization, orchestration, and prompt engineering.