Top Tech Jobs & Startup Jobs

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.