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Data Engineer (Contract, Remote)

Remote contract Data Engineer who designs, builds, and maintains ETL/ELT data pipelines and cloud data warehouse models, integrating data from APIs, databases, and third-party tools. Core stack: SQL, Python, cloud platforms (AWS/Azure/GCP), and tools like Airflow, dbt, and Spark.

Airflow Analytics API AWS Azure +23 skills

Backend Software Engineer (Python/Node/Java/Go)

Agile API AWS Azure CI/CD +22 skills

Remote backend engineer designing scalable, high-performance APIs, microservices, and event-driven systems using Python, Node.js, Java, or Go. Day to day involves database schema design and SQL optimization, cloud/CI-CD integration, security and performance tuning, and collaboration with frontend, DevOps, and product teams.

Software QA Automation Engineer

Agile API API Testing Automation BDD +17 skills

Remote QA Automation Engineer who designs and maintains automated test frameworks for web apps and APIs, builds regression/smoke/integration suites, and wires tests into CI/CD pipelines. Core stack includes Selenium/Cypress/Playwright, scripting in JavaScript/Python/Java, Postman API testing, Git, and SQL.

AI Solutions Architect

AI API AWS Azure Cloud +13 skills

Remote, leadership-level AI Solutions Architect designing end-to-end enterprise AI systems — LLM applications, RAG pipelines, and ML platforms on AWS, Azure, or GCP — while leading design reviews, setting governance and security standards, and guiding engineering teams through scalable AI deployment.

Salesforce Developer (Apex/LWC)

Apex API Automation CI/CD CPQ +7 skills

Remote Salesforce Developer designing, building, and maintaining custom Salesforce solutions: Apex classes and triggers, Lightning Web Components, SOQL/SOSL query optimization, REST/SOAP integrations, and CI/CD via Salesforce DX and Git, working alongside Salesforce consultants, architects, and business stakeholders.

Machine Learning Engineer (Production)

API AWS Azure CI/CD Cloud +21 skills

Remote machine learning engineer role focused on moving models from experimentation into production: building scalable real-time and batch inference systems, containerized deployment pipelines with CI/CD, and monitoring/retraining workflows on AWS, Azure, or GCP. Requires 4+ years of applied ML engineering with strong Python.