Posted 14 days ago

Machine Learning / AI Engineer Senior - Databricks - Eng

ArgentinaRemote

AI Summary

Senior Data Engineer at a remote startup building ML and AI systems for demand planning, forecasting, and generative AI applications, responsible for end-to-end technical delivery, MLOps, and client consulting.

About this 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 Data Engineer Senior 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).

Contribute to the strategic growth of the AI FDE Practice, including participation in technical assignments
and knowledge transfer activities.

Ensure all client engagements and training activities are properly documented and reported via designated
partner platforms.

Required Skills 💻

4+ years of hands-on professional experience developing, deploying, and managing Generative/Agentic AI
or Machine Learning systems, with a mandatory requirement for productionizing and maintaining models in
a live environment.

3+ years of experience in a customer-facing consulting or solutions architect role, focused on technical
implementation and delivery.

Excellent verbal and written communication skills for effective client and internal team interaction.

Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated
deployment best practices.

Demonstrable experience with infrastructure management, encompassing containerization (Docker) and
data pipeline orchestration.

Expertise in Python for data-intensive and scalable AI/ML applications.

Demonstrated commitment to continuous learning in emerging AI/ML fields.