{"id":3630333,"url":"https://alion.io/job/mjv-technology-and-innovation-freelancer-databricks-machine-learning-engineer","title":"Freelancer Databricks Machine Learning Engineer","company":{"id":60947,"name":"MJV Technology and Innovation","domain":"mjv.com.br","url":"https://alion.io/company/mjv-technology-and-innovation","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"InHire","truth_index":{"grade":"B","score":80,"open_postings":5,"ghost_share":0,"stale_share":0.8,"repost_share":0,"time_to_fill_p50_days":41,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"freelance","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["BR"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-28T20:02:47Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-08T23:07:41Z","board_verified":true,"closed_at":null,"days_open":11,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":11},"description":"About the Role\nWe are seeking an experienced Senior Machine Learning Engineer with deep hands-on expertise in the Databricks ecosystem to architect, build, and scale our enterprise MLOps platform. In this role, you will bridge the gap between experimental data science and resilient production engineering. You will lead the design and deployment of end-to-end ML pipelines, establish engineering best practices, and collaborate directly with international and cross-functional teams across English- and Spanish-speaking markets.\nKey Responsibilities\nEnd-to-End MLOps Pipelines: Architect and automate robust pipelines for data preparation, model training, evaluation, and automated deployment leveraging Databricks Workflows, MLflow, and Delta Lake.\n\nModel Serving & Performance Optimization: Deploy, scale, and optimize low-latency real-time endpoints and high-throughput batch pipelines using Databricks Model Serving, monitoring compute resource allocation and cold-start latency.\n\nLarge-Scale Data & Feature Engineering: Design and implement scalable data transformation workflows using PySpark and maintain reusable features using the Databricks Feature Store.\n\nGovernance & Security: Enforce end-to-end data and model lineage, role-based access control (RBAC), and auditing across all ML assets through Unity Catalog.\n\nProduction Observability: Implement comprehensive monitoring systems to track model performance, data drift, concept drift, and system health in real-time.\n\nCI/CD & Infrastructure as Code: Lead the modernization of deployment pipelines using Databricks Asset Bundles (DABs), Terraform, and CI/CD tools (GitHub Actions, GitLab CI, or Azure DevOps).\n\nCross-Regional Collaboration & Mentorship: Partner with regional technical teams, business stakeholders, and clients across the Americas, conducting technical reviews, design discussions, and mentorship in both English and Spanish.\n\nRequirements\nLanguage Proficiency\n- Full professional fluency in both English and Spanish (written and verbal) is required to collaborate with distributed regional stakeholders.\nExperience\n- 5+ years of software/ML engineering experience, with at least 3+ years actively developing on the Databricks Lakehouse platform.\nLanguages & Core\n- Advanced proficiency in Python (OOP, typing, design patterns, testing frameworks) and advanced analytical SQL.\nDistributed Computing\n- Deep hands-on knowledge of Apache Spark / PySpark, including distributed query optimization, partition tuning, memory management, and debugging skewed datasets.\nML & MLOps\n- Strong experience with MLflow (Tracking, Model Registry, Projects), plus standard ML libraries (e.g., scikit-learn, XGBoost, LightGBM, PyTorch).\nEnterprise Governance\n- Proven hands-on experience structuring environments with Unity Catalog and Delta Lake (ACID transactions, time travel, Z-ordering, Liquid Clustering).\nDevOps & Cloud\n- Proficiency in automated deployments using Databricks Asset Bundles (DABs) or Terraform, containerization (Docker), and cloud services in AWS, Azure, or 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