Senior Data Engineer (MLOps)

🏢 Summary: Senior Data Engineer role focused on large-scale migration from SQL Server to Databricks/Delta Lake, transforming complex SQL logic into production-grade Python/PySpark solutions. The position centers on enterprise-level data engineering, data modeling, and software development within a Medallion Architecture. The project involves redesigning and implementing scalable data solutions for thousands of databases in a long-term transformation initiative. 🗂️ Requirements: Very strong Python skills, Very strong PySpark skills, Proven experience with Databricks, Proven experience with Delta Lake, Strong SQL skills and ability to understand complex stored procedures, Experience working with large, shared codebases, Solid object-oriented programming knowledge, Strong data modeling experience (transactional and analytical), Experience in platform/data migration projects, Knowledge of Medallion Architecture (Bronze/Silver/Gold), Experience with large-scale data processing, Very good English skills, EU citizenship, Residence in Poland 📃 Skills: Python, PySpark, Databricks, DeltaLake, SQL, OOP, DataModeling, MedallionArchitecture, AzureDataFactory, AzureDevOps, Git, CI/CD, PowerBI 🏢 Description: In Cyclad we work with top international IT companies in order to boost their potential in delivering outstanding, cutting-edge technologies that shape the world of the future. We are seeking an experienced Senior Data Engineer with Python and Databricks. This role supports a large-scale transformation from SQL Server–based systems to a Databricks / Delta Lake platform. The focus is on enterprise-grade data engineering and software development , not analytics or reporting. The project is SQL2Databricks migration, it involves 3500-4000 SQL DBs (2TB), replicating data in different shapes/ schemas to Databricks. Project information: Type of project: IT Services Office location: Poland Work model: Remote from Poland Budget: 140 - 160 PLN net/ h - b2b Project length: till the end of 2026, possible to extend it Only candidates with citizenship in the European Union and residence in Poland Start date: ASAP Project scope: Support a large-scale transformation from SQL Server–based systems to a Databricks / Delta Lake platform Transform complex, business-critical SQL logic (stored procedures) into clean, maintainable, and scalable Python / PySpark code Redesign and implement this logic in Python / PySpark within Databricks Contribute to a large, long-running data engineering codebase used by multiple teams Develop production-grade transformation code (packages, modules, reusable components) Design and evolve data models within a Medallion Architecture (Bronze / Silver / Gold) across multiple data layers Ensure software engineering quality, reusability, and long-term maintainability Apply software engineering best practices (clean code, OOP, modularization, refactoring) Work with very large data volumes and highly parallel, event-driven transformations Actively participate in code reviews and technical design discussions Support orchestration workflows (e.g., Azure Data Factory) Competence demands: Very strong Python and PySpark skills; proven experience with Databricks and Delta Lake Experience working in large, shared codebases (beyond notebooks) Strong SQL skills, especially reading and understanding complex logic Solid object-oriented programming experience, clean code principles Strong data modelling background (transactional and analytical) Experience in redesigning models during platform migrations Familiarity with layered data architectures (Bronze / Silver / Gold) Very good English skills Nice to have: Azure Data Factory (orchestration) Azure DevOps, Git, CI/CD pipelines Power BI or analytics tooling Infrastructure / DevOps knowledge (not mandatory) We offer: Remote working model Dynamic and innovation-driven engineering environment Full-time job agreement based on b2b Private medical care with dental care (covering 70% of costs) Multisport card (also for an accompanying person) Life insurance

🏢 Summary: Remote Senior DevOps Engineer role focused on modernizing and scaling a cloud-native Data & AI Platform. The position involves refactoring AWS and Airflow infrastructure, implementing Infrastructure as Code for Snowflake, and building next-generation workflow orchestration. The role also supports CI/CD automation and infrastructure for Data Science, ML, and AI systems. 🗂️ Requirements: Strong experience with AWS, Strong experience with Terraform, Hands-on experience with Apache Airflow, Experience with CI/CD tools (GitLab, GitHub Actions, Jenkins), Experience with Snowflake and Infrastructure as Code, Knowledge of modern workflow orchestration tools (Prefect, Dagster, Temporal), Experience supporting Data Science, ML, or AI platforms 📃 Skills: AWS, Terraform, Airflow, Snowflake, GitLab, GitHubActions, Jenkins, Prefect, Dagster, Temporal, CI/CD, IaC, Python 🏢 Description: Senior DevOps Engineer (Data Platform & AI) 📍 Remote from Poland We are looking for a Senior DevOps Engineer to support the evolution of our Data & AI Platform. Key responsibilities: • Refactoring AWS-based Data Platform infrastructure • Refactoring and optimization of Apache Airflow infrastructure • CI/CD runner setup and automation • Infrastructure setup for Data Science and ML pipelines • Infrastructure as Code (Terraform) for Snowflake environments • Design and implementation of a next-generation workflow engine to replace Airflow • Infrastructure setup for AI systems, including MCP servers Requirements: • Strong AWS and Terraform experience • Hands-on experience with Airflow infrastructure • CI/CD expertise (GitLab, GitHub Actions, Jenkins) • Experience with Snowflake and Infrastructure as Code • Understanding of modern workflow orchestration tools (Prefect, Dagster, Temporal, etc.) • Experience supporting Data Science, ML, or AI platforms Nice to have: • GCP / BigQuery exposure • Experience with LLM or AI infrastructure This is an opportunity to build and modernize a cloud-native data platform while helping shape the future AI infrastructure of the organization.

🏢 Summary: B2B contract role for a Data/MLOps Engineer responsible for bridging data science and production by building and maintaining scalable ML and data pipelines on AWS. The position focuses on deploying end-to-end ML lifecycles, optimizing big data processing with Spark, and implementing robust MLOps practices. The engineer will work closely with Data Scientists to productionize models and ensure reliable, high-performance cloud infrastructure. 🗂️ Requirements: Strong ML background, Experience productionizing ML models, Proficiency in Python, Strong PySpark skills, Hands-on experience with AWS SageMaker, Experience with Apache Spark, Experience building ETL/ELT pipelines, Experience with Infrastructure as Code, Experience implementing CI/CD pipelines, Knowledge of model monitoring and drift detection, Experience working in Agile environment, English level B2 minimum 📃 Skills: Python, PySpark, PyTorch, SQL, Spark, ETL, ELT, AWS, SageMaker, CDK, Lambda, Terraform, CloudFormation, CI/CD, MLOps, IaC 🏢 Description: co.brick talents — powered by AI, powered by people. Data/MLOps Engineer (CT&C Engineering) For our Client, we are looking for a Data/MLOps Engineer to join their CT&C Engineering team. In this role, you will bridge the gap between data science and production, ensuring that scalable data solutions provide efficient ingestion, transformation, storage, and real-time analysis. If you have a strong background in ML, solid PySpark skills, and know AWS SageMaker inside out, this role is for you! Quick Job Details Rate: 140 – 150 PLN/h net Form of Cooperation: B2B Contract Start Date: ASAP English: Minimum B2 level Who Our Client Is Looking For We need a technical expert who brings overall ML background knowledge and can specifically address these core needs: The Bridge to Production: You can confidently face off with Data Scientists (who often produce notebooks only) and successfully implement their work into production-quality models. ML Model Expertise: You understand different ML models, know how to monitor them, and clearly understand their pros and cons. Hands-on Implementation: You are technically capable of building and executing these solutions using PySpark and AWS SageMaker. Technical Stack Languages & Frameworks: Python, PySpark, PyTorch, SQL Data Processing: Apache Spark, ETL/ELT Cloud & Infrastructure: AWS CDK, AWS Lambdas, AWS SageMaker, Terraform / CloudFormation Methodology & Tools: Agile, CI/CD, Training Design Key Responsibilities 1. ML & Data Infrastructure Deploy and maintain end-to-end ML lifecycles (automated training, deployment, and versioning). Build and support core MLOps components like Feature Stores, experiment tracking, and model registries. Manage scalable cloud infrastructure using Infrastructure as Code (IaC) and develop robust CI/CD/CT (Continuous Training) pipelines. 2. Data Engineering & Pipeline Optimization Build high-volume ingestion and processing pipelines using Apache Spark and PySpark. Implement data models and storage optimizations for low-latency inference and high-performance analytics. Integrate automated data quality checks and observability. 3. Governance, Security & Collaboration Proactively monitor model drift, data quality, and system latency. Maintain strict versioning for data, code, and artifacts to guarantee 100% reproducibility. Operate within an Agile framework, collaborate with Data Scientists and Product Owners, and provide clear technical documentation.

🏢 Summary: Senior Full Stack Software Engineer role focused on building and scaling a production-ready AI product, covering feature development, infrastructure, and client integrations. The position involves hands-on work with Python, DevOps/MLOps, and GenAI solutions, ensuring system reliability and production readiness. This is a remote contract role with expected extensions. 🗂️ Requirements: Strong Python development experience, DevOps expertise with CI/CD pipelines, MLOps experience with automated deployment and monitoring, Experience building agentic AI products, Experience with API integrations, Hands-on experience with GenAI/LLM, Ability to harden systems for production, Experience fine-tuning AI models, Experience with prompt engineering 📃 Skills: Python, CI/CD, DevOps, MLOps, GenAI, LLM, APIs, Automation, Deployment, Monitoring, Fine-tuning, Prompt-engineering 🏢 Description: Location : Remote with occasional visits to Łódź Employment type : 2 months initial contract - to be extended, B2B/Umowa Zlecenie Salary: up to 160zł/hour (B2B) Senior Software Engineer – AI Product (Full Stack) We’re looking for a Senior Software Engineer to help build and scale a production-ready AI product. This is a hands-on, full-stack role covering feature development, infrastructure, and client integration. What you’ll do Design, build, and deploy new features in Python Own DevOps/MLOps: CI/CD pipelines, automated model deployment, monitoring Integrate AI solutions into client-facing products Improve system reliability, performance, and production readiness Enhance AI models through fine-tuning and prompt engineering Contribute to short-term R&D initiatives and product innovation Build and evolve internal systems for model evaluation and performance tracking Must-have Strong Python development experience DevOps & MLOps expertise (CI/CD, automation, deployment) Experience building agentic AI products and API integrations Hands-on GenAI/LLM experience (classic ML not required) Proven ability to harden systems for production Nice to have Experience building model evaluation/monitoring systems Fine-tuning, prompt engineering, and performance optimization Details Contract extensions expected If you enjoy building real-world AI products end-to-end—from models to production—we’d love to hear from you.

🏢 Summary: Senior Data Platform Engineer role focused on designing, building, and operating scalable, highly available data persistence systems for distributed services. The position combines backend engineering, platform reliability, and cloud-native data infrastructure to improve performance, scalability, and observability of global data ecosystems. Hybrid work model with competitive salary and comprehensive benefits. 🗂️ Requirements: 5+ years of software engineering experience in production systems, Strong backend engineering skills (Python, Java, or Kotlin), Experience with large-scale, data-intensive systems, Solid understanding of distributed systems fundamentals, Experience in cloud environments (AWS preferred), Experience with relational or NoSQL databases, Hands-on experience with large-scale data pipelines, Experience with event-driven or streaming architectures, Understanding of end-to-end data flow (ingestion, transformation, storage, access), Experience designing scalable and reliable data systems 📃 Skills: Python, Java, Kotlin, Kafka, Spark, PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch, AWS, Terraform, ETL 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Senior Data Platform Engineer to join the Datastores team. This team is responsible for building and operating the core data persistence layer used by application services across the organization. In this role, you will design and improve the systems that store, access, and scale critical data across distributed services. It is a hands-on engineering position where you will work at the intersection of backend engineering, platform reliability, and cloud-native data infrastructure. Your work will directly influence the scalability, performance, and reliability of the company’s global data ecosystem. Key takeaways: Stack: Python, Kafka, Spark, PostgreSQL, AWS Salary: 23.000 - 30 000 PLN gross per month on Employment Contract Working model: hybrid - 3x weekly from the office Location: ul. Grzybowska 60, Warsaw Recruitment process: A call with Motife recruiter (30 min) Coding Interview (1h) Interview panel: architecture & system design discussion; Hiring Manager meeting (up to 2h in total) Responsibilities: Data Infrastructure Engineering Design, build, and operate backend systems that rely on scalable and highly available data persistence layers. Contribute to architectural decisions around distributed data systems, multi-region persistence, and global scalability. Improve the reliability and performance of production datastores used by critical services. Data Performance & Optimization Partner with service teams to improve database schema design, query performance, and data modelling. Optimize data access patterns and indexing strategies for relational and NoSQL databases. Support teams in designing systems that scale efficiently under high load. Developer Experience & Platform Tooling Build and maintain self-service tooling that enables engineers to provision and manage databases and caching layers. Contribute to infrastructure automation using tools such as Terraform and internal developer platforms. Improve observability and operational insight into datastore performance and reliability. Platform Reliability & Observability Implement monitoring, metrics, and tracing strategies to improve visibility into production data systems. Develop autoscaling and performance optimization strategies for critical data infrastructure. Support operational excellence by reducing manual processes and improving system resilience. Requirements: Technical Expertise 5+ years of experience in software engineering, building and operating production systems Strong backend engineering fundamentals (e.g. Python, Java, or Kotlin) Experience working with large-scale, data-intensive systems Solid understanding of distributed systems fundamentals (e.g. scalability, latency, reliability, data consistency) Experience working in cloud environments (preferably AWS) Familiarity with relational or NoSQL databases (e.g. PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch) Data Systems & Architecture Hands-on experience with large-scale data pipelines and data processing systems Exposure to event-driven architectures, streaming or batch processing (e.g. Kafka, Spark, ETL workflows) Understanding of end-to-end data flow: ingestion (how data enters the system) transformation (how it is processed) storage & access (how other services consume it) Experience designing systems where data performance, scalability, and reliability are critical Collaboration & Engineering Mindset Ability to work cross-functionally with service teams to improve system design and data access patterns. Strong problem-solving skills with a focus on performance, scalability, and reliability. Clear communication skills and a collaborative engineering approach. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If you’re excited about building developer platforms that scale, empower teams, and set new standards for engineering excellence, we’d love to hear from you. Apply via our careers page and please submit your CV in English .

🏢 Summary: Senior Data Platform Engineer role focused on designing, building, and operating scalable, highly available data persistence systems for distributed services. The position combines backend engineering, cloud-native infrastructure, and data platform reliability to improve performance and scalability of global data systems. It is a hands-on role working with large-scale data pipelines, streaming, and cloud environments. 🗂️ Requirements: 5+ years of software engineering experience in production systems, Strong backend programming skills in Python, Java, or Kotlin, Experience with large-scale, data-intensive systems, Solid understanding of distributed systems fundamentals, Experience with cloud environments, preferably AWS, Experience with relational or NoSQL databases, Hands-on experience with large-scale data pipelines, Experience with event-driven architectures or streaming systems, Understanding of end-to-end data flow and data modeling, Experience designing scalable and reliable data systems, Experience with infrastructure automation tools 📃 Skills: Python, Java, Kotlin, Kafka, Spark, PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch, AWS, Terraform, ETL 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Senior Data Platform Engineer to join the Datastores team. This team is responsible for building and operating the core data persistence layer used by application services across the organization. In this role, you will design and improve the systems that store, access, and scale critical data across distributed services. It is a hands-on engineering position where you will work at the intersection of backend engineering, platform reliability, and cloud-native data infrastructure. Your work will directly influence the scalability, performance, and reliability of the company’s global data ecosystem. Key takeaways: Stack: Python, Kafka, Spark, PostgreSQL, AWS Salary: 23.000 - 30 000 PLN gross per month on Employment Contract Working model: hybrid - 3x weekly from the office Location: ul. Grzybowska 60, Warsaw Recruitment process: A call with Motife recruiter (30 min) Coding Interview (1h) Interview panel: architecture & system design discussion; Hiring Manager meeting (up to 2h in total) Responsibilities: Data Infrastructure Engineering Design, build, and operate backend systems that rely on scalable and highly available data persistence layers. Contribute to architectural decisions around distributed data systems, multi-region persistence, and global scalability. Improve the reliability and performance of production datastores used by critical services. Data Performance & Optimization Partner with service teams to improve database schema design, query performance, and data modelling. Optimize data access patterns and indexing strategies for relational and NoSQL databases. Support teams in designing systems that scale efficiently under high load. Developer Experience & Platform Tooling Build and maintain self-service tooling that enables engineers to provision and manage databases and caching layers. Contribute to infrastructure automation using tools such as Terraform and internal developer platforms. Improve observability and operational insight into datastore performance and reliability. Platform Reliability & Observability Implement monitoring, metrics, and tracing strategies to improve visibility into production data systems. Develop autoscaling and performance optimization strategies for critical data infrastructure. Support operational excellence by reducing manual processes and improving system resilience. Requirements: Technical Expertise 5+ years of experience in software engineering, building and operating production systems Strong backend engineering fundamentals (e.g. Python, Java, or Kotlin) Experience working with large-scale, data-intensive systems Solid understanding of distributed systems fundamentals (e.g. scalability, latency, reliability, data consistency) Experience working in cloud environments (preferably AWS) Familiarity with relational or NoSQL databases (e.g. PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch) Data Systems & Architecture Hands-on experience with large-scale data pipelines and data processing systems Exposure to event-driven architectures, streaming or batch processing (e.g. Kafka, Spark, ETL workflows) Understanding of end-to-end data flow: ingestion (how data enters the system) transformation (how it is processed) storage & access (how other services consume it) Experience designing systems where data performance, scalability, and reliability are critical Collaboration & Engineering Mindset Ability to work cross-functionally with service teams to improve system design and data access patterns. Strong problem-solving skills with a focus on performance, scalability, and reliability. Clear communication skills and a collaborative engineering approach. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If you’re excited about building developer platforms that scale, empower teams, and set new standards for engineering excellence, we’d love to hear from you. Apply via our careers page and please submit your CV in English .

🏢 Summary: Senior Data Platform Engineer role focused on designing, building, and operating scalable, highly available data persistence systems for distributed services. The position combines backend engineering, cloud-native infrastructure, and data platform reliability to support global, data-intensive applications. The engineer will enhance performance, scalability, and observability of production datastores across a multi-region environment. 🗂️ Requirements: 5+ years of software engineering experience in production systems, Strong backend programming skills (Python, Java, or Kotlin), Experience with large-scale, data-intensive systems, Solid understanding of distributed systems fundamentals, Experience with cloud environments, preferably AWS, Hands-on experience with relational or NoSQL databases, Experience with large-scale data pipelines and data processing systems, Experience with event-driven architectures or streaming/batch processing, Understanding of end-to-end data flow (ingestion, transformation, storage, access), Experience designing scalable, high-performance, reliable data systems 📃 Skills: Python, Java, Kotlin, Kafka, Spark, PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch, AWS, Terraform, ETL 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Senior Data Platform Engineer to join the Datastores team. This team is responsible for building and operating the core data persistence layer used by application services across the organization. In this role, you will design and improve the systems that store, access, and scale critical data across distributed services. It is a hands-on engineering position where you will work at the intersection of backend engineering, platform reliability, and cloud-native data infrastructure. Your work will directly influence the scalability, performance, and reliability of the company’s global data ecosystem. Key takeaways: Stack: Python, Kafka, Spark, PostgreSQL, AWS Salary: 23.000 - 30 000 PLN gross per month on Employment Contract Working model: hybrid - 3x weekly from the office Location: ul. Grzybowska 60, Warsaw Recruitment process: A call with Motife recruiter (30 min) Coding Interview (1h) Interview panel: architecture & system design discussion; Hiring Manager meeting (up to 2h in total) Responsibilities: Data Infrastructure Engineering Design, build, and operate backend systems that rely on scalable and highly available data persistence layers. Contribute to architectural decisions around distributed data systems, multi-region persistence, and global scalability. Improve the reliability and performance of production datastores used by critical services. Data Performance & Optimization Partner with service teams to improve database schema design, query performance, and data modelling. Optimize data access patterns and indexing strategies for relational and NoSQL databases. Support teams in designing systems that scale efficiently under high load. Developer Experience & Platform Tooling Build and maintain self-service tooling that enables engineers to provision and manage databases and caching layers. Contribute to infrastructure automation using tools such as Terraform and internal developer platforms. Improve observability and operational insight into datastore performance and reliability. Platform Reliability & Observability Implement monitoring, metrics, and tracing strategies to improve visibility into production data systems. Develop autoscaling and performance optimization strategies for critical data infrastructure. Support operational excellence by reducing manual processes and improving system resilience. Requirements: Technical Expertise 5+ years of experience in software engineering, building and operating production systems Strong backend engineering fundamentals (e.g. Python, Java, or Kotlin) Experience working with large-scale, data-intensive systems Solid understanding of distributed systems fundamentals (e.g. scalability, latency, reliability, data consistency) Experience working in cloud environments (preferably AWS) Familiarity with relational or NoSQL databases (e.g. PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch) Data Systems & Architecture Hands-on experience with large-scale data pipelines and data processing systems Exposure to event-driven architectures, streaming or batch processing (e.g. Kafka, Spark, ETL workflows) Understanding of end-to-end data flow: ingestion (how data enters the system) transformation (how it is processed) storage & access (how other services consume it) Experience designing systems where data performance, scalability, and reliability are critical Collaboration & Engineering Mindset Ability to work cross-functionally with service teams to improve system design and data access patterns. Strong problem-solving skills with a focus on performance, scalability, and reliability. Clear communication skills and a collaborative engineering approach. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If you’re excited about building developer platforms that scale, empower teams, and set new standards for engineering excellence, we’d love to hear from you. Apply via our careers page and please submit your CV in English .

🏢 Summary: B2B Senior Test Engineer role focused on ensuring quality, reliability, and performance of Azure-based Data Hubs and data pipelines, with emphasis on automated testing and big data validation. The position involves building test frameworks, validating ETL workflows, and integrating testing into CI/CD processes. Work is hybrid with regular on-site presence in Warsaw. 🗂️ Requirements: Strong expertise in Python, Strong expertise in PySpark, Strong expertise in SQL, Experience with test automation frameworks, Experience with Azure Data Services (Databricks, Data Factory, Storage Accounts), Experience in testing big data pipelines and ETL workflows, Hands-on experience with CI/CD tools, Knowledge of data governance and data quality frameworks, Ability to work in Agile/Scrum environment 📃 Skills: Python, PySpark, SQL, Azure, Databricks, DataFactory, AzureDevOps, Jenkins, CI/CD, ETL, BigData, DevOps 🏢 Description: contract: B2B rate: 145 pln/h + VAT mode: 2x a month from the office, Warsaw As a Senior Test Engineer, you will be responsible for ensuring the quality, reliability, and performance of Data Hubs and data processing pipelines in Azure & Databricks. You will develop automated test frameworks, conduct regression testing, and validate large-scale data processing workflows. How you will get the job done ·       designing, developing, and maintaining automated test frameworks for data processing and orchestration. ·       conducting regression, performance, and scalability testing for data ingestion and transformation. ·       implementing data quality validation and anomaly detection in Azure-based data pipelines. ·       collaborating with Data Engineers and DevOps teams to integrate testing into CI/CD pipelines. ·       ensuring compliance with data security, governance, and privacy standards. ·       defining test strategies, best practices, and documentation for Data & AI solutions Skills and experience you will need ·       strong expertise in Python, PySpark, SQL, and test automation frameworks. ·       experience with Azure Data Services (Databricks, Data Factory, Storage Accounts). ·       proficiency in testing big data pipelines, distributed systems, and ETL workflows. ·       hands-on experience with CI/CD tools (Azure DevOps, Jenkins) and test automation. ·       strong understanding of data governance, quality frameworks, and monitoring tools. ·       excellent problem-solving skills and ability to work in Agile/Scrum environments. Nice to Have ·       experience with ML model validation and AI pipeline testing ·       familiarity with data lineage tracking and metadata management tools

🏢 Summary: Remote Senior MLOps/Cloud Engineer role supporting an LLM/NLP project, focused on building and maintaining scalable ML infrastructure in the cloud. The position involves deploying, monitoring, and optimizing machine learning systems using modern DevOps and MLOps practices. Responsibilities include CI/CD implementation, container orchestration, and cloud resource optimization. 🗂️ Requirements: Experience with at least one cloud provider and related services, Experience with monitoring and logging tools, Experience with infrastructure-as-code solutions, Experience with microservices architecture, Experience with Docker and Kubernetes, Experience with ML/MLOps stack including Kubeflow and MLFlow, Experience with data versioning tools, Experience with GPU acceleration techniques, Experience with multiple database types, Experience with cloud resource optimization, Experience with CI/CD pipelines for ML models, Proficiency in Python or Bash, Experience with container orchestration platforms, Knowledge of cloud security best practices 📃 Skills: AWS, Azure, GCP, Prometheus, Grafana, ELK, Terraform, Docker, Kubernetes, Kubeflow, MLflow, Python, Bash, GPU, ECS, EKS, Swarm, CI/CD 🏢 Description: We are currently looking for a Senior MLOps/Cloud Engineer to support an LLM/NPL Project. Must-have experiences with: · One or more cloud providers and their services · Monitoring and logging tools (e.g. Prometheus, Grafana, ELK stack) · Infrastructure-as-code solutions/templates · Microservices, Docker and Kubernetes · The data science stack, including Kubeflow, MLFlow, data versioning tools, GPU acceleration techniques, and multiple types of databases · Optimizing cloud resources effectively · Implementing and maintains CI/CD pipelines for ML models · Python or Bash for automation tasks · Container orchestration platforms like Docker Swarm or Amazon ECS/EKS · Best security practices in cloud environments Location: 100% remote Language: English, German (nice to have)

🏢 Summary: Senior Data Engineer role in the banking sector focused on building scalable Big Data solutions supporting Financial Crime Prevention processes. The position involves developing distributed data pipelines and streaming applications using Spark and Scala in a Continuous Delivery environment. It is a long-term hybrid project with an immediate start. 🗂️ Requirements: Senior-level experience in Big Data and ETL, Senior-level Scala proficiency, Senior-level Spark and Spark SQL expertise, Strong SQL knowledge, Experience with Data Testing, Practical experience with distributed large-scale data systems 📃 Skills: Scala, Spark, SparkSQL, SQL, ETL, BigData, DataTesting 🏢 Description: 📍 Location: Gdańsk / Gdynia / Warsaw (hybrid) 🚀 Start: ASAP 💶 Rate: 35–45 EUR/h About the role We are looking for a Senior Data Engineer to work on a long-term project for a leading client in the banking industry . In this role, you will implement complex data requirements and build scalable solutions that support the client’s Financial Crime Prevention processes . Responsibilities Build distributed Big Data processing pipelines handling large volumes of structured and unstructured data in near real-time Develop streaming applications connecting multiple internal and external data sources Transform and enrich data with Apache Spark for analytics, visualization, and search Deliver clean, high-quality datasets for downstream systems Work closely with DevOps, QA, and Product teams in a Continuous Delivery environment Requirements Experience with Big Data / ETL / SQL / Data Testing (Senior level) Senior-level Scala Senior-level Spark and Spark SQL Practical experience with distributed, large-scale data systems Recruitment process First interview: Conversation with our Talent Team Technical interview: Discussion with one of our developers Final interview: Meeting with the CTO