Jobs at MUTT DATA

Build and operate production machine learning and data pipelines on Databricks. Responsibilities include orchestrating Jobs workflows, managing Databricks Asset Bundle deployments across environments and countries, administering MLflow and Unity Catalog model lifecycles, monitoring compute costs and observability, and diagnosing runtime incidents. The role requires advanced Python, SQL, Delta, Unity Catalog, CI/CD, Azure DevOps, Git, infrastructure as code, and Databricks platform expertise.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs; guide domain teams on data modeling and product boundaries; mentor engineers and analysts; and improve reusable platform templates, tooling, and engineering practices.

Build and govern scalable machine learning features and AI solutions using Databricks and Unity Catalog. Responsibilities include feature engineering, metadata and lineage management, data governance, CI/CD deployment pipelines, job monitoring, FinOps, and maintaining consistency between training and production environments. The role requires advanced SQL and Python skills, strong data engineering expertise, and knowledge of data quality and scalable architecture practices.

Evaluate and deploy low-code and agent-builder platforms such as n8n, integrating them with cloud infrastructure and corporate systems. Build custom Python and JavaScript/TypeScript integrations, connectors, and automation workflows; implement containers, Terraform, CI/CD, secrets, networking, security controls, and observability. Validate solutions technically before production adoption and operate platforms autonomously for enterprise teams.

Lead and mentor data scientists while architecting Python-based machine learning solutions, deploying and monitoring production models, applying advanced statistical methods, and translating business problems into actionable data science plans. The role also involves communicating findings to stakeholders, developing junior talent, identifying innovative opportunities, and supporting scalable client delivery.

Design, deploy, monitor, and maintain machine learning models for forecasting, recommendations, pricing, churn, and other business applications. Perform exploratory data analysis, define metrics with stakeholders, build scalable ML workflows, communicate insights, collaborate cross-functionally, mentor junior staff, and promote reproducible data science practices.

Design, develop, and maintain scalable backend services in Go. Contribute to architectural decisions, microservices development, hexagonal architecture, reusable components, code reviews, and technical standards while collaborating with an AI-focused team. The role also requires daily use of AI-assisted development tools and openness to evolving AI-driven workflows.

Design, develop, and maintain Java backend services within microservices architectures. Contribute to architectural decisions, technical standards, modular system design, reusable components, and code reviews. Apply hexagonal or layered architecture principles while collaborating with an AI-focused team. The role also requires daily use of AI-assisted development tools and adaptability to AI-driven workflows. Experience with AI agents is preferred.

Design and govern transactional and credit behavior features for a credit-risk model using Databricks and Unity Catalog. Build reusable, auditable features with comprehensive metadata, lineage, ownership, lifecycle, and quality standards. Follow deployment, monitoring, and FinOps practices while ensuring training-production consistency. Collaborate with modeling and platform teams, leveraging advanced SQL and Python. Experience with financial data, Feature Store certification, and AI agent infrastructure is beneficial.

Own and evolve a live credit scoring model using tree ensembles and gradient boosting. Retrain the model, add new data features, evaluate AUC-ROC, F1, and calibration, segment portfolios by probability of default, and calculate dynamic credit lines and expected loss. Package the model for production using the MFL framework, PyFunc, model cards, and tests while collaborating with data and platform teams.

Lead end-to-end data migration initiatives across cloud data platforms, including architecture decisions, dependency mapping, cutover planning, validation, risk mitigation, and stakeholder alignment. Build and optimize batch and streaming data pipelines using Databricks, Python, and Spark. Maintain migration documentation, testing strategies, rollback plans, and operational procedures while promoting data governance, quality, and delivery best practices.

Build and deploy LLM-based agents for banking clients using LangGraph, LiteLLM, and Langfuse. Develop backend services, API integrations, guardrails, evaluations, observability, and production infrastructure on AWS. Collaborate with security, networking, DevSecOps, and business teams to resolve delivery issues. Create documentation and runbooks, transfer ownership to client teams, and improve reusable agent templates, deployment pipelines, and platform observability after each embedded engagement.

Industrialize, deploy, monitor, and scale machine learning models in production. Build training, inference, retraining, CI/CD, model tracking, and model-serving pipelines. Use MLflow for versioning and governance, Databricks for workflow orchestration, Docker and Kubernetes for deployment, and observability practices for reliable ML operations. Collaborate with data scientists, data engineers, and business stakeholders to deliver scalable, production-grade ML systems.

Migrate Power BI dashboard data sources to Databricks Datalake views, validate data integrity and business-critical metrics, and prevent volumetric discrepancies. Design optimized DAX queries and complex business logic while collaborating with technical support and dashboard design teams. The role requires advanced Power BI, SQL, Databricks, Unity Catalog, and data validation expertise, along with autonomy in deadline-driven migration projects.

Lead technical direction for a platform team building modern backend and data-stack solutions for ML products. Provide technical leadership, evaluate architectures, unblock teams (backend or data stack), use AI tools in workflows, document decisions via ADRs/RFCs, and support modernization with legacy/on-prem constraints.

Design and build scalable Java backend services for reporting, dashboards, and analytics. Contribute to architecture and technical documentation, collaborate with Product and Design, implement testing and monitoring, ensure production reliability, and mentor engineers while improving backend development practices.

Industrialize, deploy, monitor, and scale machine learning models in production. Design training, inference, and retraining pipelines; build CI/CD workflows; manage MLflow tracking, registries, versioning, and governance; develop scalable model-serving APIs; orchestrate Databricks workflows; and containerize applications with Docker for Kubernetes deployment. Collaborate with data scientists, data engineers, and stakeholders to establish reliable MLOps architecture and operational practices.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs for data products. Guide domain teams on data modeling and product boundaries, mentor engineers and analysts, and improve platform templates, tooling, and engineering practices.

The Data Engineer will design and implement ETL processes, optimize data queries, automate pipelines, and develop scalable data infrastructures.

The Data Engineer Lead will oversee team collaboration, lead projects, design ETL processes, mentor members, and optimize data infrastructure.