Senior Data Scientist

🏢 Summary: Staff Data Scientist (Tech Lead) role focused on building and scaling real-time AI-driven decisioning systems to improve product conversion. The position combines hands-on machine learning leadership with technical ownership of production ML pipelines and experimentation strategy. You will drive advanced ML initiatives including recommendation systems, uplift modeling, and LLM-based personalization in low-latency environments. 🗂️ Requirements: 10+ years experience in Data Science with production ML systems, Expert-level Python and SQL, Strong experience with PySpark or Apache Spark, Experience building real-time ML systems with strict latency constraints, Proven experience with gradient boosting and recommendation systems, Strong background in A/B testing and causal inference, Experience building ML pipelines on AWS, Hands-on experience with LLMs including fine-tuning and evaluation, Experience with feature engineering and model monitoring in production 📃 Skills: Python, SQL, PySpark, Spark, NumPy, Pandas, Scikit-learn, XGBoost, LightGBM, AWS, Databricks, LLMs, NLP, Clustering, Uplift, Bandits, ABTesting, CausalInference 🏢 Description: We are looking for a Staff Data Scientist to join a Growth Alliance team at the Tech Lead level. This role goes beyond hands-on data science, it requires a candidate who can set technical direction, lead complex ML initiatives, and act as a strategic partner to business stakeholders. You will operate at the intersection of advanced machine learning, real-time systems, and product growth. About the Project: This is a Staff / Tech Lead position within a team driving AI-powered decisioning at scale. You will work on systems with strict real-time constraints (<40 ms latency) that directly impact end-user experience and conversion. The mission is to move the product toward AI-driven decisioning, targeting a measurable improvement in conversion (~5%). You will be expected to challenge assumptions, define the technical roadmap, and ensure that ML systems perform reliably in production. Responsibilities: Lead the design and implementation of production ML systems, including real-time decisioning and personalization engines; Drive adoption of contextual bandits, uplift modeling, and advanced recommendation frameworks; Define experimentation strategy including A/B testing, causal inference, and synthetic controls; Oversee feature engineering at scale and ensure robust model monitoring and validation practices; Act as a technical owner for decisioning platforms and Martech integrations; Collaborate with and influence senior business stakeholders to shape data science strategy; Guide and mentor other data scientists on the team; Lead GenAI and LLM-based initiatives including fine-tuning, evaluation, and content personalization. Qualifications: 10+ years of experience in Data Science, with a strong track record in production ML systems; Expert-level Python (NumPy, Pandas, Scikit-learn) and SQL; Deep experience with PySpark / Apache Spark for large-scale data processing; Mastery of gradient boosting methods (XGBoost, LightGBM), recommendation systems, NLP, clustering, and uplift modeling; Extensive experience with A/B testing, causal inference, statistical modeling, and synthetic controls; Proven ability to build and maintain production ML pipelines on cloud platforms (AWS preferred); Experience with decisioning platforms, personalization engines, and Martech integrations; Hands-on experience with LLMs, prompt engineering, evaluation, and fine-tuning; Strong stakeholder management skills with the ability to challenge assumptions and ask the right questions; Databricks experience is a nice to have. Recruitment Process: Initial interview with our recruitment team; Interview with the hiring manager; Live Coding Assessment; Meeting with the Project Manager. Inuits Sp. z o.o. is registered in the National Register of Employment Agencies (KRAZ) under number 35420.

🏢 Summary: Senior Data Scientist role focused on leading the design and delivery of production-grade Generative AI solutions using LLMs, including RAG pipelines, chatbots, and AI agents. The position combines hands-on development with architectural decision-making and technical mentorship, delivering scalable GenAI systems for large enterprise clients. You will own the full lifecycle from solution discovery through deployment, safety, and optimization. 🗂️ Requirements: 6+ years experience in Data Science or AI engineering, 4+ years production experience with Python for AI/ML, 2+ years building and deploying LLM-based systems in production, Experience designing and implementing RAG pipelines, Experience with model fine-tuning techniques (LoRA, QLoRA, SFT), Experience building end-to-end GenAI applications including orchestration and APIs, Ability to design system architectures for LLM-based solutions, Experience implementing AI safety and governance controls, Strong analytical and problem-solving skills, Professional English communication skills 📃 Skills: Python, LLMs, LangChain, LlamaIndex, LangGraph, RAG, LoRA, QLoRA, SFT, APIs, VectorDB, AI, MachineLearning 🏢 Description: ABOUT THE COMPANY Our client is an end-to-end data services partner to global enterprises, founded in 2008 and headquartered in Warsaw. Our teams work with over 75 leading consumer packaged goods brands across more than 30 countries, helping them unlock the full value of their data — from strategy and development through to operations and adoption. Our work spans supply chain analytics, customer analytics, AI and machine learning, data platforms, and digital commerce. We are recognised as a Strong Performer in the Gartner Peer Insights Voice of the Customer report for data and analytics, and hold Great Place to Work certification in multiple countries. ABOUT THE ROLE We're looking for an experienced Senior Data Scientist with deep expertise in Generative AI to lead projects building and implementing advanced LLM-based systems — including chatbots, AI agents, and RAG pipelines. This is a technical leadership role: you'll make architectural decisions, select technologies, set best practices, and mentor other engineers, while remaining hands-on across the full solution lifecycle. You'll work closely with data engineers, product owners, and full-stack developers to deliver scalable GenAI applications for large enterprise clients, primarily in CPG, retail, and manufacturing. WHAT YOU'LL WORK ON Solution design and discovery Lead discovery and solution design for GenAI use cases — translating business problems into concrete architectures covering LLM selection, RAG, fine-tuning, agents, and guardrails. End-to-end GenAI applications Build complete GenAI solutions covering data ingestion, retrieval layers, orchestration (LangChain, LlamaIndex, LangGraph), API and backend, and lightweight UI where needed. RAG pipeline design Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness. Model selection, prompting, and fine-tuning Own prompting strategies, model selection, and fine-tuning (LoRA, QLoRA, SFT) for text, code, and multimodal models, including evaluation and A/B testing. Safety and governance Implement safety, compliance, and governance controls including input/output filters, PII handling, audit logs, and human-in-the-loop review where required. Requirements and estimation Gather technical requirements from stakeholders and produce reliable estimates for planned work. Mentorship and knowledge sharing Mentor other data scientists and engineers in GenAI patterns, code quality, and best practices. Contribute to internal libraries, templates, and reusable components. Staying current Track the GenAI landscape — new open and hosted models, agentic frameworks, evaluation techniques — and run targeted PoCs to validate emerging approaches. WHAT WE LOOK FOR 6+ years in Data Science or AI engineering Broad experience across the data science and AI stack, with a track record of delivering production systems. 4+ years of production Python for AI Fluent in writing production-ready Python for AI and ML workloads — clean, maintainable, and deployable. 2+ years of production LLM development Hands-on experience building and shipping LLM-based systems in production environments, not just research or prototypes. Strong analytical and problem-solving skills Able to break down ambiguous problems, make sound architectural decisions under uncertainty, and defend those decisions clearly. Excellent English communication Comfortable working directly with international clients and cross-functional teams. Able to translate technical complexity for non-technical stakeholders. THE TEAM You'll join a specialist Data Science and AI practice working alongside experienced consultants, ML engineers, and data engineers. The team delivers solutions for large international clients across CPG, retail, and manufacturing. There is a strong knowledge-sharing culture, with internal communities, competency centres, and structured learning programmes built into how the team operates. COMPENSATION & BENEFITS Rate 105 – 170 PLN per hour on a B2B contract, depending on experience. Work model Fully remote or office-based — your choice. Flexibility on working hours and contract form. Workation policy Option to work remotely from other locations for defined periods. Onboarding Comprehensive online onboarding programme with a dedicated buddy from day one. Learning and development Unlimited access to the Udemy learning platform from day one. Certificate training programmes, upskilling support, capability development programmes, competency centres, knowledge sharing sessions, community webinars, and over 110 training opportunities per year. Career growth Internal promotion pathways — 76% of managers were promoted internally. Cooperation with top-tier engineers and domain experts across the organisation. Referral bonuses Financial rewards for successful employee referrals. Wellbeing Activities to support health and wellbeing, with opportunities to contribute to charitable causes and environmental initiatives. Equipment Modern office equipment provided. Employer recognition Great Place to Work certified employer.

🏢 Summary: Senior Machine Learning Engineer role focused on building and scaling end-to-end recommendation systems for personalized content and workout experiences. The position involves developing real-time ML services, running experiments, and deploying production-grade models in a cloud-based environment. You will contribute to unified recommendation architecture and LLM-driven personalization initiatives. 🗂️ Requirements: 4+ years of experience in machine learning, Experience in recommender systems or applied ML domains, Degree in Computer Science, Machine Learning, Statistics, Mathematics or related quantitative field, Hands-on experience building, training and evaluating ML models, Strong software engineering skills, Professional experience with Python, Experience with large-scale data pipelines, Experience with distributed data processing, Experience with Spark and Airflow, Experience with AWS or similar cloud environment, Experience with Kubernetes and production serving technologies, Experience with A/B testing and experiment analysis 📃 Skills: Python, Spark, Airflow, AWS, S3, Kubernetes, RecommenderSystems, DeepLearning, Transformers, LLMs, MLOps, REST, gRPC, FastAPI, ABTesting 🏢 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 Machine Learning Engineer to join the Personalization team, which owns the recommendation systems powering content discovery across the ecosystem. In this role, you will work on end-to-end ML systems: building and training models, improving real-time recommendation services, running experiments, and deploying scalable solutions into production. You will help evolve the recommendation platform from multiple models toward a more unified, real-time architecture, while also contributing to company's IQ initiatives involving LLMs, personalized plans, and AI-powered member experiences. Key takeaways: Stack : Python, Spark, Airflow, AWS, S3, Kubernetes, recommender systems, Deep Learning, Transformers, LLMs, MLOps, REST/gRPC/FastAPI, A/B testing Salary : 31 000 PLN - 35 000 PLN gross on the Contract of Employment Working model : Hybrid - 3 days/week from the office Location : ul. Grzybowska 60, Warsaw Recruitment process : A call with MOTIFE Recruiter Hiring Manager screening Coding interview Panel interviews with the team (coding, architecture, and cross-collaboration interviews, Hiring Manager meeting) Responsibilities: Build, train, evaluate, and improve ML models powering personalized workout and content recommendations. Develop and maintain data and ML pipelines using Python, Spark, Airflow, AWS, and S3. Productionize, deploy, monitor, and optimize ML models and recommendation services. Improve real-time model serving, including latency, scalability, reliability, and infrastructure costs. Support the evolution of recommendation systems toward unified rankers, candidate generation, and inference services. Run A/B tests and analyze experiment results with Product Analysts to measure product and member impact. Contribute to IQ initiatives, including personalized plans, insights, LLM-based features, and responsible AI practices. Collaborate with ML Engineers, Data Engineers, Software Engineers, Platform Engineers, Product Managers, and Analysts to deliver scalable personalization features. Requirements: 4+ years of experience in machine learning, ideally in recommender systems, NLP, computer vision, or another applied ML domain. Degree in Computer Science, Machine Learning, Statistics, Mathematics, Operational Research, or another quantitative field. Strong hands-on experience building, training, evaluating, and improving ML models. Solid software engineering skills, including clean code, data structures, algorithms, and production readiness. Professional experience with Python; experience with Java, Kotlin, Go, C, or C++ is a plus. Experience with large-scale data pipelines, distributed processing, and orchestration tools such as Spark and Airflow. Familiarity with cloud-based ML environments, preferably AWS, and production serving technologies such as Kubernetes, REST, gRPC, or FastAPI. Strong communication skills and the ability to work cross-functionally with product, analytics, platform, data, and engineering teams. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If this sounds like your next step, we’d love to hear from you! Please apply via our careers page and submit your CV in English.

🏢 Summary: The role focuses on building and operating scalable, low-latency machine learning infrastructure for real-time personalization services. You will design and maintain Python-based microservices, deploy ML models to cloud environments, and ensure high availability and performance of recommendation systems. The position involves close collaboration with ML and platform teams to bring ML-powered features into production. 🗂️ Requirements: 3+ years of professional software engineering experience, Degree in Computer Science, Engineering, or related technical field, Strong knowledge of data structures and algorithms, Professional experience building backend services in Python, Experience designing RESTful APIs or gRPC services, Experience building microservices architectures, Experience deploying production services on AWS, GCP, or Azure, Experience with relational and non-relational databases, Experience with event-driven architectures and message queues, Strong debugging, profiling, and performance tuning skills 📃 Skills: Python, AWS, GCP, Azure, Kubernetes, Docker, REST, gRPC, Kafka, RabbitMQ, SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog, Grafana, MLflow 🏢 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 Machine Learning Infrastructure Engineer to join the Personalization team, which owns the systems powering content recommendations across the company’s digital ecosystem. In this role, you will design, build, and maintain low-latency, highly scalable services that make real-time personalization possible. You will work hands-on with backend services, cloud infrastructure, model serving, observability, and performance optimization, partnering closely with ML Engineers, API Engineers, Platform Engineers, and Product Managers to bring ML-powered product features into production. Key takeaways: Stack : Python, AWS/GCP/Azure, Kubernetes, Docker, REST, gRPC, Kafka/RabbitMQ/SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog/Grafana/MLflow Salary : 24 000 PLN - 28 000 PLN gross on the Contract of Employment Working model : Hybrid - 3 days/week from the office Location : ul. Grzybowska 60, Warsaw Recruitment process : A call with MOTIFE Recruiter Hiring Manager screening Coding interview Panel interviews with the team (coding, architecture, and cross-collaboration interviews, Hiring Manager meeting) Responsibilities: Design, build, and maintain Python microservices powering personalized content recommendations. Productionize, deploy, monitor, and operate machine learning services in cloud-based production environments. Partner with ML Engineers to integrate models into scalable backend services and real-time recommendation workflows. Ensure high availability, low latency, and strong performance through caching, load balancing, auto-scaling, and capacity planning. Own and improve personalization services, including reliability, testability, observability, scalability, and operational readiness. Conduct performance tuning, profiling, and latency optimization for high-traffic recommendation workloads. Collaborate with platform teams to use infrastructure, tooling, and deployment workflows that support fast product iteration. Work with Product Managers, ML Engineers, API Engineers, and Data Engineers to launch ML-powered personalization features. Requirements: 3+ years of professional software engineering experience and a degree in Computer Science, Engineering, or a related technical field. Strong software engineering fundamentals, including data structures, algorithms, clean code, testing, and reproducibility. Professional experience building backend services in Python; experience with Java, Kotlin, Go, C, or C++ is welcome. Experience designing and building RESTful APIs, gRPC services, or microservices from the ground up. Strong experience deploying and managing production services on AWS or GCP, or Azure. Experience with relational and non-relational databases such as Postgres, MySQL, DynamoDB, or Redis. Experience with event-driven architectures and message queues such as Kafka, RabbitMQ, or SQS. Strong debugging, profiling, and performance tuning skills, including latency tracking, scalability analysis, and production troubleshooting. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If this sounds like your next step, we’d love to hear from you! Please apply via our careers page and submit your CV in English.

🏢 Summary: The role involves building and operating scalable, low-latency machine learning infrastructure to power real-time personalization and content recommendations. You will design and maintain Python-based microservices, deploy ML models to cloud environments, and ensure high availability and performance of production systems. The position focuses on backend engineering, cloud infrastructure, and model serving within a cross-functional ML product team. 🗂️ Requirements: 3+ years of professional software engineering experience, Degree in Computer Science, Engineering, or related technical field, Strong knowledge of data structures and algorithms, Professional experience building backend services in Python, Experience designing RESTful APIs or gRPC services, Experience building microservices architectures, Experience deploying production services on AWS, GCP, or Azure, Experience with relational and non-relational databases, Experience with event-driven architectures and message queues, Strong debugging, profiling, and performance tuning skills 📃 Skills: Python, AWS, GCP, Azure, Kubernetes, Docker, REST, gRPC, Kafka, RabbitMQ, SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog, Grafana, MLflow 🏢 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 Machine Learning Infrastructure Engineer to join the Personalization team, which owns the systems powering content recommendations across the company’s digital ecosystem. In this role, you will design, build, and maintain low-latency, highly scalable services that make real-time personalization possible. You will work hands-on with backend services, cloud infrastructure, model serving, observability, and performance optimization, partnering closely with ML Engineers, API Engineers, Platform Engineers, and Product Managers to bring ML-powered product features into production. Key takeaways: Stack : Python, AWS/GCP/Azure, Kubernetes, Docker, REST, gRPC, Kafka/RabbitMQ/SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog/Grafana/MLflow Salary : 24 000 PLN - 28 000 PLN gross on the Contract of Employment Working model : Hybrid - 3 days/week from the office Location : ul. Grzybowska 60, Warsaw Recruitment process : A call with MOTIFE Recruiter Hiring Manager screening Coding interview Panel interviews with the team (coding, architecture, and cross-collaboration interviews, Hiring Manager meeting) Responsibilities: Design, build, and maintain Python microservices powering personalized content recommendations. Productionize, deploy, monitor, and operate machine learning services in cloud-based production environments. Partner with ML Engineers to integrate models into scalable backend services and real-time recommendation workflows. Ensure high availability, low latency, and strong performance through caching, load balancing, auto-scaling, and capacity planning. Own and improve personalization services, including reliability, testability, observability, scalability, and operational readiness. Conduct performance tuning, profiling, and latency optimization for high-traffic recommendation workloads. Collaborate with platform teams to use infrastructure, tooling, and deployment workflows that support fast product iteration. Work with Product Managers, ML Engineers, API Engineers, and Data Engineers to launch ML-powered personalization features. Requirements: 3+ years of professional software engineering experience and a degree in Computer Science, Engineering, or a related technical field. Strong software engineering fundamentals, including data structures, algorithms, clean code, testing, and reproducibility. Professional experience building backend services in Python; experience with Java, Kotlin, Go, C, or C++ is welcome. Experience designing and building RESTful APIs, gRPC services, or microservices from the ground up. Strong experience deploying and managing production services on AWS or GCP, or Azure. Experience with relational and non-relational databases such as Postgres, MySQL, DynamoDB, or Redis. Experience with event-driven architectures and message queues such as Kafka, RabbitMQ, or SQS. Strong debugging, profiling, and performance tuning skills, including latency tracking, scalability analysis, and production troubleshooting. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If this sounds like your next step, we’d love to hear from you! Please apply via our careers page and submit your CV in English.

🏢 Summary: Lead Data Engineer role focused on building scalable AI and cloud-based data solutions in a global environment. The position involves leading data engineering initiatives, designing secure cloud architectures, and supporting AI applications using AWS or Azure. The offer includes hybrid work, professional development opportunities, and a comprehensive benefits package. 🗂️ Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience, 8+ years in software engineering, 5+ years of backend development with Python, Experience designing and scaling complex data systems, Hands-on experience with AI technologies, Experience with AWS or Azure, Strong knowledge of Python and SQL, Experience with APIs, data integration, automation tools, and data governance, Understanding of data security and compliance standards, Leadership and mentoring experience, English and Polish proficiency (minimum B2) 📃 Skills: Python, SQL, AWS, Azure, Java, APIs, DevOps, AI, RAG, MCP, Automation, DataGovernance 🏢 Description: We are looking for an experienced Lead Data Engineer to drive AI and cloud-based data solutions within a global technology organization. In this role, you will lead data initiatives, shape scalable architectures, and collaborate with both technical teams and senior stakeholders to deliver secure and high-performing systems. Key Responsibilities: Act as the main point of contact for data access and system-related topics with senior stakeholders Lead and mentor data engineers, promoting best practices and technical excellence Design, build, and maintain scalable cloud infrastructure and data pipelines Ensure data quality, security, compliance, and governance across the full lifecycle Develop secure and reliable cloud architectures (AWS/Azure) for AI and enterprise applications Implement monitoring, alerting, disaster recovery, and business continuity solutions Take technical ownership of applications within a DevOps environment Drive automation and self-service capabilities Support AI initiatives (e.g., AI Agents, RAG, MCP) with focus on quality and scalability Stay updated on emerging technologies and advise on strategic data direction Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience 8+ years in software engineering, including 5+ years of backend development with Python (production level) Strong experience designing and scaling complex data systems Hands-on experience with AI technologies and cloud platforms (AWS or Azure) Solid knowledge of Python, SQL (Java is a plus) Experience with APIs, data integration, automation tools, and data governance Strong understanding of data security and compliance standards Proven leadership and mentoring experience Excellent communication skills in English and Polish (min. B2); German is a plus What We Offer: Opportunity to work in a global, international environment Real impact on AI and cloud solutions in a large-scale organization Access to training platforms and professional development programs Hybrid work model with flexible hours (modern office in central Wroclaw) Comprehensive benefits package (medical & dental care, sports card, life insurance, mental health program) Cafeteria benefits platform with monthly points CSR initiatives, integration events, and employee passion clubs

🏢 Summary: Lead Data Engineer role focused on driving AI initiatives and building scalable cloud-based data architectures in a global environment. The position involves technical ownership of data platforms, designing secure and high-performing systems, and leading data engineering efforts in a DevOps setting. The role emphasizes AI integration, cloud infrastructure, and enterprise-grade data governance. 🗂️ Requirements: Degree in Computer Science, AI, Data Science, Software Engineering or equivalent experience, 8+ years in software engineering, 5+ years of backend development with Python in production, Strong experience designing and scaling complex data systems, Hands-on experience with AI technologies, Hands-on experience with AWS or Azure, Strong knowledge of Python and SQL, Experience with APIs and data integration, Experience with automation tools, Knowledge of data governance practices, Understanding of data security and compliance standards, Proven experience leading and mentoring engineers 📃 Skills: Python, SQL, AWS, Azure, Java, AI, RAG, MCP, APIs, DevOps, Automation, Monitoring, Cloud, DataEngineering, DataPipelines, Governance, Security, Compliance, Backend 🏢 Description: We are looking for an experienced Lead Data Engineer to drive AI and cloud-based data solutions within a global technology organization. In this role, you will lead data initiatives, shape scalable architectures, and collaborate with both technical teams and senior stakeholders to deliver secure and high-performing systems. Key Responsibilities: Act as the main point of contact for data access and system-related topics with senior stakeholders Lead and mentor data engineers, promoting best practices and technical excellence Design, build, and maintain scalable cloud infrastructure and data pipelines Ensure data quality, security, compliance, and governance across the full lifecycle Develop secure and reliable cloud architectures (AWS/Azure) for AI and enterprise applications Implement monitoring, alerting, disaster recovery, and business continuity solutions Take technical ownership of applications within a DevOps environment Drive automation and self-service capabilities Support AI initiatives (e.g., AI Agents, RAG, MCP) with focus on quality and scalability Stay updated on emerging technologies and advise on strategic data direction Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience 8+ years in software engineering, including 5+ years of backend development with Python (production level) Strong experience designing and scaling complex data systems Hands-on experience with AI technologies and cloud platforms (AWS or Azure) Solid knowledge of Python, SQL (Java is a plus) Experience with APIs, data integration, automation tools, and data governance Strong understanding of data security and compliance standards Proven leadership and mentoring experience Excellent communication skills in English and Polish (min. B2); German is a plus What We Offer: Opportunity to work in a global, international environment Real impact on AI and cloud solutions in a large-scale organization Access to training platforms and professional development programs Hybrid work model with flexible hours (modern office in central Wroclaw) Comprehensive benefits package (medical & dental care, sports card, life insurance, mental health program) Cafeteria benefits platform with monthly points CSR initiatives, integration events, and employee passion clubs

🏢 Summary: Senior Python Engineer role focused on building production-grade data pipelines and AI-driven systems, including LLM-based applications. The position involves designing scalable architectures, processing large datasets, and integrating modern cloud data platforms. You will work on complex, real-world data solutions used in production environments. 🗂️ Requirements: Senior-level Python experience, Strong knowledge of SQL and data processing, Experience building and maintaining data pipelines, Hands-on experience with orchestration tools (Airflow, Prefect, or Dagster), Experience with cloud data platforms (S3, BigQuery, Snowflake, or Databricks), Experience integrating LLM frameworks (LangChain or LlamaIndex), Ability to design scalable, production-ready systems 📃 Skills: Python, Pandas, NumPy, PySpark, SQL, Airflow, Prefect, Dagster, S3, BigQuery, Snowflake, Databricks, LangChain, LlamaIndex, LLM 🏢 Description: Our client is a technology consultancy that builds advanced data and AI systems for startups and enterprise companies. Their focus is on delivering production-ready solutions, especially in areas like data platforms, large-scale processing, and LLM-based applications. The Role As a Senior Python Engineer, you’ll be working on complex, data-heavy systems that go beyond prototypes. This includes designing and developing data pipelines, handling large-scale datasets, and integrating modern AI solutions (including LLMs) into real-world products. You’ll collaborate closely with experienced engineers and take part in architectural decisions, helping turn ideas into scalable, reliable systems used in production. What we’re looking for We’re looking for a strong senior-level engineer with deep experience in Python (including libraries like Pandas, NumPy, and PySpark), as well as solid knowledge of SQL and data processing. You should be comfortable working with data pipeline orchestration tools such as Airflow, Prefect, or Dagster, and have hands-on experience with modern cloud data platforms (e.g. S3, BigQuery, Snowflake, or Databricks). Experience with LLM frameworks like LangChain or LlamaIndex, especially in the context of data integration, is an important part of this role. Why join This is a great opportunity to work on real AI and data systems at scale, in a consultancy environment where engineering quality matters and you have a genuine impact on technical decisions. You’ll join a senior, low-ego team, work fully remotely from Poland, and contribute to projects for international clients.

🏢 Summary: The offer is for an experienced Java Developer to build and integrate AI-driven backend solutions, including AI agents and LLM-powered services, within scalable distributed systems. The role focuses on designing reasoning pipelines, vector search integrations, and data-heavy architectures using modern AI tooling. You will contribute to architecture design, code quality, and AI-based system development in a collaborative environment. 🗂️ Requirements: Minimum 3 years of professional Java experience, Strong backend development skills, Experience with Spring Boot, Experience with Docker, Experience with Spring AI, Experience integrating LLMs into applications, Understanding of vector databases, Experience with AWS Bedrock, Experience with LangChain4j, Knowledge of embeddings, Knowledge of prompt engineering basics, Knowledge of retrieval-augmented generation (RAG), Understanding of Model Context Protocol, Experience building scalable distributed systems, Experience designing AI agents and reasoning pipelines 📃 Skills: Java, SpringBoot, Docker, SpringAI, OpenAI, Anthropic, Milvus, Pinecone, Qdrant, Elasticsearch, AWS, Bedrock, LangChain4j, Embeddings, RAG, MCP 🏢 Description: We are looking for an experienced Java Developer with strong backend engineering skills and proven experience in AI-related technologies and frameworks. The ideal candidate has at least 3 years of professional Java experience, writes clean, maintainable code, and is comfortable working on scalable distributed systems. A strong interest in data, AI-driven applications, vector search, and modern LLM tooling is highly valued. We are looking for a team player who thrives in collaborative environments, enjoys knowledge sharing, and contributes actively to technical discussions and solution design. Key Responsibilities Designing and implementing AI agents with reasoning pipelines (e.g. multi-step workflows, tool usage, RAG-based decision making) Integrating AI capabilities such as LLM-powered services, semantic search, and intelligent automation Contributing to scalable architectures for data- and event-driven systems Improving, refactoring, and maintaining existing codebases Designing tasks in collaboration with the Team Lead and Product Owner Participating in code reviews, architecture discussions, and knowledge sharing Contribute to the design of scalable architectures for data-heavy and AI-driven services Collaborate closely with the Team Lead and Product Owner to design tasks and shape technical solutions Participate in code reviews, architecture discussions, and knowledge-sharing sessions Required Skills & Technology: English min B2 + Polish or C1 Experience - minimum 3 years Java: Spring Boot Docker AI-related: Spring AI Experience integrating LLMs into applications (OpenAI API, Anthropic, local inference, etc.) Understanding of vector databases (Milvus, Pinecone, Qdrant, Elasticsearch vector search, or similar) AWS Bedrock LangChain4j Knowledge of embeddings, prompt engineering basics, and retrieval-augmented generation (RAG) Understanding Model Context Protocol Nice to Have: Redis Mongo DB AI-related: Experience with AI/ML frameworks or orchestration libraries (LangChain4j, Spring AI, Embabel, Haystack, etc.) Familiarity with LLM model lifecycle: prompt design, evaluation, latency considerations, cost/performance trade-offs Experience with Ollama / vLLM Experience with streaming architectures for AI pipelines (i.e. Kafka Streams) Experience with document processing, OCR, or semantic search Perks in the office: Modern office full of coffee, snacks, and beverages Bike parking, great location of the office Benefits: Training budget for self-development, courses, conferences, and more A smart benefits platform that lets you easily manage subscriptions and purchases — including sports and lunch cards. Flat structure and small teams International project Team Events Private healthcare, Life & group insurance We celebrate birthdays and other important employee events Recruitment process: Screening (15-30 min) -> We want to get to know you and answer your questions - it is up to you how much time we need. Recruiting Task - Show your skills :) We will send you a task, you decide how much time you need to complete it. Video call (1h/2h)-> Let's talk about your coding style, exchange experiences, and get to know each other a little better. Data Sharing - the best way to better data. We are passionate about new technologies and we constantly improve our stack. There is always enough room to learn the ropes as we provide internal and external training and education. We are looking for a person who loves high-quality of code and who is interested in big sets of data. 90% of the world's data has been produced in just the last two years. Our mission is to help companies with our innovative Data Quality Services. CDQ is the first company in the market to leverage Data Sharing as the best way to better data. Our customers share data quality rules, enrich their data from publicly shared sources, exchange hand-validated data records, and alert each other of data fraud. The result of collaborative master data management: Higher efficiency, shared efforts, better quality, less risks. With our cloud-based software platform, CDQ can provide Data Quality as a Service – customers' master data is monitored and updated continuously, using our Data Quality Tools software or fully integrated with their ERP or CRM systems.

🏢 Summary: Senior Research Engineer role focused on designing, developing, and deploying production-ready machine learning models for search, ranking, and ads at scale. The position involves end-to-end ML ownership, from data exploration and experimentation to evaluation and production deployment, while translating state-of-the-art research into business impact. The role combines applied ML research with large-scale production collaboration. 🗂️ Requirements: Master’s or PhD in Machine Learning, Mathematics, Computer Science, Statistics, or related STEM field, Strong knowledge of deep learning techniques, Experience in at least one domain: Information Retrieval or Natural Language Processing, Ability to design and conduct scientific experiments using iterative methodologies, Experience working with complex, real-world datasets, Proficiency in Python, Experience with ML model development libraries (PyTorch or TensorFlow) 📃 Skills: Python, PyTorch, TensorFlow, Transformers, Pandas, NumPy, DeepLearning, NeuralNetworks, ContrastiveLearning, SemiSupervisedLearning, InformationRetrieval, NLP, LLMs, GCP, AWS, Azure, Kubernetes, Docker, Consul, GitHub, GitHubActions 🏢 Description: In the Learning to Rank team we develop machine learning models for search, ranking and ads. Our models serve millions of searches a day. We develop and apply state-of-the-art machine learning methods, helping Allegro grow and innovate with artificial intelligence. Beyond bringing AI to production, we are committed to advance the understanding of machine learning through open collaboration with the scientific community. About the role: As a Senior Research Engineer, you will work at the intersection of cutting-edge machine learning research and real-world products used at scale. You’ll take ownership of ML solutions end to end - from exploring complex data and selecting the right methods, to rigorous evaluation and production deployment. This role is for someone who enjoys navigating uncertainty, keeping pace with a fast-moving ML landscape, and turning research insights into tangible business impact, while also shaping ML expertise within the team and beyond. In your daily work you will handle the following tasks End-to-end ML ownership: Design and deliver production-ready machine learning solutions for Allegro products Data exploration: Analyze and understand complex data sets, identifying relevant data sources for ML use cases Model development & evaluation: Train, evaluate, and experiment with applied ML models using reliable evaluation methods Applied ML research: Translate state-of-the-art ML research into practical improvements for real-world solutions Tooling & methodology: Select ML tools and techniques that best fit concrete business needs Production collaboration: Work closely with cross-functional teams to deploy ML solutions into production Knowledge sharing: Spread ML expertise through internal sessions, presentations, and research activities Mentorship & expertise: Support junior team members and act as a trusted ML expert within the organization We are looking for people who Have a master's or PhD in machine learning, mathematics, computer science, statistics or other STEM fields Have a good knowledge of deep learning techniques (neural networks, contrastive learning, semi-supervised learning) in at least one domain (information retrieval, natural language generation or understanding, etc.) Know the methodology of conducting scientific research and the use of iterative processes of conducting experiments Have experience in working with real data that deviate from the standard, well-developed collections used in research Know Python and libraries necessary to work with model development (PyTorch, Tensorflow, Transformers, Pandas, Numpy, etc.) Nice-to-have: Understanding of Information Retrieval (IR) area Published research papers in ML-related topics Experience in business cooperation, deployments of ML-based solutions in commercial environments Prior experience in running large-scale computation on cloud platform (GCP, AWS or Azure) Prior experience in using LLMs for synthetic data generation/solving business problems What's in it for you Flexible working hours in the hybrid model (4/1) - working hours start between 7:00 a.m. and 10:00 a.m. We also have 30 days of occasional remote work. Long term discretionary incentive plan based on Allegro.eu shares (restricted stock units). Annual bonus based on your annual performance and company results. Well-located offices (with e.g. fully equipped kitchens, bicycle parking, terraces full of greenery) and excellent work tools (e.g., raised desks, ergonomic chairs, interactive conference rooms). A 16" or 14" MacBook Pro or corresponding Dell with Windows (if you don't like Macs) and all the necessary accessories. A wide selection of fringe benefits in a cafeteria plan - you choose what you like (e.g., medical, sports or lunch packages, insurance, purchase vouchers). English classes that we pay for related to the specific nature of your job. A training budget, inter-team tourism ( see more here ), hackathons, and an internal learning platform where you will find multiple trainings. An additional day off for volunteering, which you can use alone, with a team, or with a larger group of people connected by a common goal. Social events for Allegro people - Spin Kilometers, Family Day, Fat Thursday, Advent of Code, and many other occasions we enjoy. And that's just the beginning! You can read more about the benefits here . #goodtobehere means that: You will join a team you can count on - we work with top-class specialists who have knowledge- and experience-sharing in their DNA. You will love our level of autonomy in team organization, the space for continuous development, and the opportunity to try new things. You get to choose which technology solves the problem and you are responsible for what you create. You will value our Developer Experience and the full platform of tools and technologies that make creating software easier. We rely on an internal ecosystem based on self-service and widely used tools such as Kubernetes, Docker, Consul, GitHub, and GitHub Actions. Thanks to this, you can contribute to Allegro from your very first days on the job. You will be equipped with modern AI tools to automate repetitive tasks, allowing you to focus on developing new services and refining existing ones (also leveraging AI support). You will create solutions that will be used (and loved!) by your friends, family and millions of our customers. You will meet the Allegro Scale , which starts with over 1000 microservices, an open-source data bus (Hermes) with 300K+ rps, a Service Mesh with 1M+ rps, tens of petabytes of data, and production-used machine learning. You will become part of Allegro Tech - We speak at industry conferences, cooperate with tech communities, run our own blog (it's been over 10 years!), record podcasts, lead guilds, and we organize our own internal conference - the Allegro Tech Meeting. We create solutions we love (and can) to talk about! Send us your CV and… see you at Allegro!