Huge is seeking a Solutions Engineer to join our team. The Solutions Engineer brings a broad technical knowledge across multiple systems and solutions, with deep expertise in one or more areas. They can quickly grasp problems and provide recommendations that have an immediate impact on projects. While a Solutions Engineer may have a specialization, they are expected to have a broad range of skills and experience across the full stack.

The ideal candidate will have a strong understanding of enterprise systems and a proven track record in designing and implementing solutions that deliver business value. As a Solutions Engineer, you will collaborate closely with stakeholders to understand their needs, architect solutions, and ensure successful delivery and integration.

What We’d Like to See:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• 5+ years of experience in a senior engineering role, with a focus on machine learning, artificial intelligence, or data technologies.
• Strong programming skills in languages such as Python, Java, or C++, with proficiency in data and ML/AI frameworks, libraries (e.g., pandas, numpy, LangChain), and data analytics tools.
• A deep understanding of enterprise system architecture, ensuring compliance and efficiency within the framework, and the ability to break down solutions to demonstrate system functionality.
• Knowledge of DevOps processes, tools, and practices, with the ability to evaluate and recommend new code and DevOps security tools.
• Clear understanding of AI techniques such as Agents, RAG, semantic routing, and caching.
• Experience working with cloud-based AI services and third-party models (e.g., Vertex AI, OpenAI, Vector databases, HuggingFace, Stable Diffusion).
• Familiarity with Neural Network implementations (CNNs, RNNs, GANs, Transformers, etc.) is a plus.
• Extensive experience with cloud platforms: AWS, Azure, or Google Cloud, including deploying ML/AI solutions in cloud environments; experience with Gen AI is a plus.
• Relevant certifications (e.g., AI/ML/Data certifications, cloud certifications) are a plus.
• Excellent client-facing skills with the ability to build rapport and trust.
• Strong problem-solving skills with the ability to think strategically and creatively.
• Excellent communication and presentation skills to explain complex technical concepts to non-technical stakeholders.
• A strong appreciation for design and user experience and its importance in the final solution.

What You’ll Be Doing:

• Apply common design patterns and best practices, drive coding standards, lead code reviews, and ensure engineering excellence across multiple projects.
• Quickly identify critical capabilities, gaps, and architectural roadblocks, and communicate them to the team, providing alternative approaches.
• Create functional proof of concepts (POCs) and work hands-on with AI frameworks and tools to explore potential solutions.
• Lead the implementation of applications using modern AI technologies such as Generative AI for enterprise use cases.
• Implement data pipelines, data preprocessing, and feature engineering processes to support AI model training and inference.
• Quickly learn new technologies, stay updated with the latest advancements and industry trends, and share that knowledge with the team and clients.
• Communicate effectively with other disciplines, teammates, and stakeholders to resolve issues, present architectural designs clearly, and manage architectural discussions with client, partner, and vendor organizations.
• Mentor junior engineers, provide thought leadership and expert guidance on technologies, educate clients on best practices and emerging trends, and conduct training sessions, workshops, and presentations.
• Ensure code is properly tested by yourself and others, troubleshoot and resolve technical issues to guarantee system reliability and scalability.
• Focus on individual systems and problems, lead problem-solving activities, and mitigate issues as needed.
• Lead work sessions to develop creative solutions, and proactively refactor and iterate over the project lifecycle to achieve simplicity.
• Work towards providing and discovering technical solutions to address identified risks and continuously seek opportunities to enhance solution performance and client satisfaction.
• Drive the adoption of security as code across all projects where you are leading the solution architecture.