Role Description We are seeking a talented AI Engineer to join our team and bridge the gap between innovative ideas and real-world applications. In this role, you will focus on applying existing AI models and tools to develop practical systems, rather than creating new AI algorithms from scratch. You will be responsible for building robust, scalable applications that leverage large language models (LLMs), full-stack development, cloud technologies, and rapid prototyping techniques. The ideal candidate thrives in a fast-paced environment, excels at turning concepts into functional prototypes quickly, and has a strong command of tools like LangChain and LangGraph to streamline AI workflows. - 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. Qualifications - 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) and backend (e.g., PHP and Laravel, 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. Requirements - 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. Benefits - Competitive salary and benefits package. - Opportunity to work on cutting-edge AI projects that impact real-world applications. - Flexible work environment with remote options. - Professional development opportunities, including access to conferences and training.

• You will design, build, test, and deploy AI-powered solutions aligned to client business requirements. • Lead the design, build, test, and deployment of AI agents, assistants, and workflow automations aligned to client business objectives using platforms such as Microsoft Copilot Agent Builder, Azure AI Foundry, Azure OpenAI, and the Anthropic API. • Own the end-to-end lifecycle of AI solutions, from requirements and architecture through deployment and optimization. • Navigate technical solutioning discussions with client and internal stakeholders; translate requirements into scalable AI architectures. • Analyze, document, and map current-state (as-is) business processes, identifying pain points, inefficiencies, and opportunities for automation and optimization. • Design future-state (to-be) processes that integrate AI capabilities, workflow automation, and decision support. • Collaborate with solution architects, developers, and subject matter experts to translate process requirements into actionable technical designs.

• Complete onboarding and get familiar with our tools and process. • Get to know the team. • Work with colleagues to release small features and bug fixes. • Be responsible for releasing features to production on a rapid agile cadence • Take ownership and lead a technical project; • Or, become an expert on a specific technology or product domain.

• Collaborate and advise on next-generation AI-assisted agentic workflows strategy alongside product owners, developers and creatives. • Design and own the architecture for AI-powered frameworks and automation across the entire Studio pipeline and platforms. • Define the end-to-end architecture for ingestion, processing, and routing of operational telemetry (metrics, logs, traces). • Integrate solutions with popular industry DCC tools. • Prototype solutions for high-friction tasks: asset tagging, scene prep, render queue management, shot setup, and versioning. • Define technical standards and integration patterns for AI tooling across departments and shows. • Evaluate and select AI frameworks, foundation models, and automation technologies for creative production and operational stability. • Build and maintain a library of reusable AI components and automation patterns. • Partner with platform, systems and security teams to monitor model drift, system latency, and system security. • Write, test, and deploy production-ready tools that run reliably inside active show pipelines. • Develop reference implementations that set the canonical pattern for future pipeline AI work. • Partner with engineers and TDs to fold automation into existing artist-facing toolsets. • Maintain and iterate on deployed tools based on artist feedback and production data. • Drive adoption across both technical and creative departments so tools are understood, trusted, and used. • Create lightweight training and documentation for artists and engineers. • Establish metrics for automation impact and report results to studio leadership on a regular cadence. • Champion a culture of continuous improvement and surface new automation opportunities as workflows evolve.