MUTT DATA

• Design, build, and maintain production recommendation systems at consumer scale. • Develop ranking and retrieval models to improve personalization and user experience. • Own ML pipelines end-to-end, from feature engineering and training to deployment and monitoring. • Collaborate with client stakeholders and engineering teams to deliver scalable ML solutions. • Analyze large-scale user behavior data and run A/B tests to optimize recommendation performance. • Improve reliability, scalability, and latency of production ML systems on AWS/GCP/Azure.

• Design and build backend services for reporting, dashboards, and analytics. • Ensure high standards of code quality, scalability, reliability, and performance. • Participate in architectural decisions and document technical designs. • Collaborate with Product and Design to deliver high-impact features. • Implement testing and monitoring to ensure production stability. • Mentor engineers and contribute to improving backend development practices.

• Help create and publish job postings on our website and job boards. • Assist in sourcing potential candidates using ATS and external platforms. • Support in reviewing candidate applications and shortlisting profiles. • Keep the Lever ATS updated with candidate status, salary expectations, and other key info. • Schedule interviews and coordinate with interviewers. • Ensure a smooth and positive candidate experience through timely communications. • Collaborate with hiring teams to track interview feedback and next steps. • Assist in drafting and reviewing offer letters. • Learn and apply our internal recruitment processes and tools.

• Design and implement AI agents and multi-agent systems for real-world business use cases. • Build and maintain agentic workflows, including orchestration, tool usage, and inter-agent communication. • Develop integrations between agents and enterprise systems (APIs, databases, SaaS platforms). • Work with semantic layers and contextual data models to improve agent reasoning and performance. • Implement and manage MCP-like architectures, ensuring proper context sharing and tool connectivity. • Collaborate with data and platform teams to leverage Databricks and modern data stacks. • Ensure solutions are production-ready, scalable, and aligned with governance and security requirements. • Translate business problems into agent-based AI solutions with clear technical execution paths. • Stay up to date with the latest advancements in agentic AI frameworks and LLM ecosystems.

• Collaborate with the team to define goals and deliver custom data solutions. • Innovate with new tools to improve infrastructure and processes. • Design and implement ETL processes, optimize queries, and automate pipelines. • Own projects end-to-end, working directly with clients. • Mentor and support team members, helping them deepen their knowledge in Data Engineering and model design. • Lead data pipeline development and guide technical decisions. • Act as a liaison between the team and stakeholders, ensuring alignment. • Foster team autonomy while ensuring smooth collaboration. • Contribute to technical research and stay ahead of solutions for future projects. • Focus on code quality—review, document, test, and integrate CI/CD. • Participate in candidate interviews and evaluations to help Mutt grow.

• Collaborate with the team to define goals and deliver custom data solutions. • Innovate with new tools to improve infrastructure and processes. • Design and implement ETL processes, optimize queries, and automate pipelines. • Own projects end-to-end, working directly with clients. • Mentor and support team members, helping them deepen their knowledge in Data Engineering and model design. • Lead data pipeline development and guide technical decisions. • Act as a liaison between the team and stakeholders, ensuring alignment. • Foster team autonomy while ensuring smooth collaboration. • Contribute to technical research and stay ahead of solutions for future projects. • Focus on code quality—review, document, test, and integrate CI/CD. • Participate in candidate interviews and evaluations to help Mutt grow.

• Develop and optimize ML models, ensuring scalability, monitoring, and integration with MLOps best practices. • Implement client requirements, from exploratory data analysis (EDA) to feature engineering and model lifecycle management. • Build ML Proof of Concepts (POCs) to validate and refine solutions. • Optimize models for performance, latency, memory, and throughput. • Apply statistical analysis techniques and develop regression models. • Design and maintain feature stores and data pipelines for ML workflows. • Research and implement emerging ML/AI techniques to enhance solutions. • Collaborate with stakeholders to align technical solutions with business needs.

• Take full ownership of new connector requests, driving them from initial requirement to production enablement. • Lead and push homologation processes with Security and other internal stakeholders until approval is achieved. • Identify and resolve technical and administrative bottlenecks that slow down adoption. • Coordinate with business and technology teams to ensure use cases progress effectively. • Ensure connectors are not only approved but fully operational and usable in real scenarios. • Provide technical clarity around integrations and connector architecture. • Actively drive adoption of low-code solutions across teams. • Lay the groundwork for future advanced automation initiatives.

• Contribute to the implementation of modern data pipelines for diverse data sources, ensuring scalability and performance. • Research and develop new technologies, tools, and best practices to continuously improve Mutt Data’s infrastructure and capabilities. • Collaborate with cross-functional teams to ensure smooth integration of data systems with existing workflows and tools.

• Collaborate with the team to define goals and deliver custom data solutions. • Innovate with new tools to improve Mutt Data's infrastructure and processes. • Design and implement ETL processes, optimize queries, and automate pipelines. • Own projects end-to-end—build, maintain, and improve data systems while working with clients. • Develop tools for the team and assist in tech migrations and model design. • Build scalable, high-performance data architectures. • Focus on code quality—review, document, test, and integrate CI/CD.