Senior AI Engineer

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Requirements:

• Demonstrable experience in applied AI, with a foundation in machine learning, NLP, LLMs, and statistical analysis.
• Strong understanding of the trade-offs between various generative AI models and the ability to choose the right model for specific use cases.
• Experience with data embeddings and vector databases, understanding the trade-off between available options, and leveraging it to optimize data ingestion.
• Experience in architecting and developing solutions that integrate generative AI with traditional software solutions with minimal to no oversight.
• Experience building and testing a server-side platform for API development and orchestration.
• Is proficient in the Python language and understands the trade-offs between multiple frameworks and patterns.
• Skilled in creating and adjusting prompts for complex AI systems to meet diverse project requirements.
• Familiarity with testing and evaluating AI systems using state-of-the-art methods and best practices.
• Hands-on experience deploying software on leading cloud platforms and utilizing AI tools like AWS Bedrock, Azure AI Services, and Vertex AI.
• Strong collaboration skills and ability to work alongside developers from multiple different areas.
• Ability to communicate complex AI solutions and concepts effectively to technical and non-technical stakeholders.

Responsibilities:

• Apply your knowledge of AI systems and software engineering to develop solutions that directly address and resolve business problems.
• Partner with professionals from Data Science and Data Engineering to address complex technical challenges, ensuring that the latest and most effective Data & AI techniques are being utilized.
• Take ownership of implementing and optimizing applied AI components, ensuring they meet project needs with high complexity and scale.
• Navigate and manipulate generative AI models, including (but not limited to) LLMs, to create prompts and solutions tailored to specific use cases.
• Develop and incorporate AI solutions while adhering to industry best practices, including moderation, security, monitoring, and compliance standards.
• Understand and properly apply Responsible AI concepts in all the stages of the solution.
• Lead the charge in designing, measuring, and evaluating AI model outputs, developing standard and custom metrics to ensure alignment with business objectives.
• Translate AI research and PoCs into production-ready features, delivering robust and scalable AI components that integrate seamlessly with larger systems.
• Drive the selection and application of appropriate evaluation metrics, ensuring that AI solutions are robust, unbiased, and meet all necessary performance standards.