#16 Machine Learning / AI Engineer Lead - Databricks - Eng

About the role

🚀 Join Our Data Products and Machine Learning Development Remote Startup! 🚀

Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.

We’re looking for a Machine Learning / AI Engineer Lead to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect! 🐶🚀

🚀 What We Do - Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.

- Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.

- Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.

- Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.

- Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.

- Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.

🌟 Our Partnerships - Amazon Web Services

- Astronomer

- Databricks

🌟 Our Values - 📊 We are Data Nerds

- 🤗 We are Open Team Players

- 🚀 We Take Ownership

- 🌟 We Have a Positive Mindset

🔍 Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on! 🚀

Responsibilities 🤓 - Serve as a primary technical consultant, leading and executing end-to-end AI/ML project implementations directly with clients, translating complex business problems into robust technical solutions.

- Exhibit excellent communication, presentation, and stakeholder management skills to clearly articulate technical findings, proposals, and project status to both technical and non-technical audiences.

- Design, build, and maintain production-grade AI and ML pipelines, focusing on continuous integration, continuous delivery (CI/CD), and advanced MLOps/AIOps practices to ensure reliability and scalability of models.

- Implement and optimize cutting-edge Generative and Agentic AI applications, demonstrating hands-on experience with technologies like multi-agent orchestration, agent evaluation, and Retrieval Augmented Generation(RAG) Systems in a production setting.

- Architect end-to-end AI/ML systems, designing both traditional Machine Learning- and Generative AI-based solutions based on customer requirements

- Manage underlying solution infrastructure necessary for production deployment, demonstrating proficiency in technologies such as Docker, pipeline orchestrators, and lakehouse database systems like Databricks.

- Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high- performance data processing (e.g., using technologies like Apache Spark).