Senior AI Engineer

Webook

Key Responsibilities:

• Design, develop, and deploy full-stack applications that integrate AI models, with a focus on LLMs for tasks such as natural language processing, generation, and automation.
• Collaborate with cross-functional teams to rapidly prototype and iterate on ideas, ensuring quick turnaround from concept to production-ready solutions.
• Utilize LangChain and LangGraph to build efficient, stateful AI agents and multi-step workflows that apply existing models to solve real-world problems.
• Implement cloud-based infrastructure (e.g., AWS, Azure, GCP) for hosting, scaling, and managing AI applications, including data pipelines, APIs, and deployment pipelines.
• Participate in live coding sessions and rapid development sprints to demonstrate and refine solutions in real-time.
• Optimize applications for performance, security, and reliability, ensuring seamless integration of AI components into user-facing systems.
• Troubleshoot and debug complex issues across the stack, from frontend interfaces to backend AI logic and cloud deployments.
• Stay updated on emerging AI tools and best practices to continuously improve development processes and application efficiency.

• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related field (or equivalent experience).
• Minimum 10+ years of experience in full-stack development, with proficiency in frontend (e.g., React, Vue, Angular) and backend (e.g., Node.js, Python, Django, Flask, FAST API) technologies.
• Working knowledge of leading AI coding platforms and tools, including Claude, OpenAI Cursor, and similar frameworks for building and integrating AI-driven applications
• Strong expertise in working with Large Language Models (LLMs) such as GPT, BERT, or similar, including fine-tuning, prompting, and integration into applications.
• Hands-on experience with LangChain for chaining LLM calls and LangGraph for building graph-based AI applications.
• Proven ability in cloud platforms (e.g., AWS, Azure, GCP), including services for compute, storage, databases, and AI/ML (e.g., SageMaker, Vertex AI).
• Demonstrated skills in live coding and rapid prototyping to quickly validate and implement ideas.
• Solid understanding of software engineering principles, including version control (Git), CI/CD pipelines, and agile methodologies.
• Excellent problem-solving skills and the ability to work independently or in a team to deliver high-quality results under tight deadlines.
• Experience with additional AI frameworks like Hugging Face Transformers, TensorFlow, or PyTorch.
• Knowledge of containerization (Docker) and orchestration (Kubernetes) for deploying AI applications at scale.
• Familiarity with data engineering tools (e.g., Apache Airflow, Spark) for handling AI data pipelines.
• Background in DevOps practices to automate infrastructure and deployment processes.
• Strong communication skills to explain technical concepts to non-technical stakeholders.