Fullstack Senior Data Scientist

🏢 Summary: Senior Data Scientist role focused on building and operating real-time, AI-driven decisioning systems that directly impact user experience and conversion rates. The position involves developing and deploying production-grade machine learning models with strict low-latency requirements and contributing to personalization, experimentation, and recommendation systems. It combines hands-on ML engineering with experimentation, causal analysis, and large-scale data processing in a business-facing environment. 🗂️ Requirements: 7+ years commercial experience in Data Science with production ML focus, Strong experience deploying and maintaining production ML models, Experience with real-time systems and low-latency constraints, Proficiency in Python and SQL, Hands-on experience with gradient boosting methods, Experience with recommendation systems and uplift modeling, Strong knowledge of A/B testing and causal inference, Experience building feature engineering pipelines at scale, Experience with Apache Spark or PySpark, Cloud platform experience (AWS preferred), Familiarity with LLMs and GenAI for personalization 📃 Skills: Python, SQL, NumPy, Pandas, Scikit-learn, XGBoost, LightGBM, PySpark, Spark, AWS, Databricks, NLP, LLM, GenAI 🏢 Description: We are looking for a Senior Data Scientist to join a Growth Alliance team focused on AI-driven decisioning and user-facing machine learning systems. This role is suited for a technically strong data scientist who is equally comfortable working with business stakeholders and operating production ML systems at scale. About the Project: This is a high-impact role working directly on systems that influence end-user experience and conversion. You will operate in a real-time environment with strict low-latency requirements (<40 ms), contributing to a shift toward AI-driven decisioning aimed at improving product performance and increasing conversion rates. The work spans production ML pipelines, experimentation, and personalization, not just research or experimentation in isolation. Responsibilities: Design, develop, and deploy production machine learning models with real-time constraints; Build and maintain recommendation systems, uplift models, and contextual bandit frameworks; Conduct A/B testing, causal inference analyses, and statistical hypothesis testing; Develop and maintain feature engineering pipelines at scale using PySpark / Apache Spark; Monitor and validate deployed models to ensure performance and reliability; Collaborate closely with business stakeholders to challenge assumptions and define the right problems; Contribute to generative AI and LLM-based personalization initiatives; Work with decisioning platforms, personalization engines, and Martech integrations. Qualifications: 7+ years of commercial experience in Data Science with a strong production ML focus; Proficiency in Python (NumPy, Pandas, Scikit-learn) and SQL; Hands-on experience with gradient boosting methods (XGBoost, LightGBM); Experience with recommendation systems, clustering, NLP, and uplift modeling; Solid background in A/B testing frameworks, causal inference, and statistical modeling; Experience with feature engineering and production ML pipelines at scale; Cloud platform experience, preferably AWS; Familiarity with LLMs, prompt engineering, and GenAI for content personalization; Strong communication skills and ability to work in a highly business-facing environment; 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 Engineer role focused on building and optimizing data integration and retrieval solutions that power Generative AI systems, including AI agents and RAG pipelines. The position involves designing scalable data pipelines and Python-based microservices integrated with large-scale GenAI platforms. The engineer will contribute to architecture and ensure high performance, reliability, and security of data services. 🗂️ Requirements: 5+ years of experience in software or data engineering with strong focus on Python, Hands-on experience with FastAPI and microservices, Experience with API-driven architectures, Experience with databases or data platforms (MongoDB, PostgreSQL, Hadoop, BigQuery, Starburst or similar), Understanding of vector stores, embeddings or semantic search concepts, Experience with cloud platforms (GCP or Azure), Solid understanding of Unix/Linux environments, Strong debugging skills 📃 Skills: Python, FastAPI, Microservices, APIs, MongoDB, PostgreSQL, Hadoop, BigQuery, Starburst, GCP, Azure, Linux, Unix, Vectorstores, Embeddings, SemanticSearch, Debugging 🏢 Description: Senior Data Engineer (Python / GenAI) 📍 Location: Kraków, Poland (hybrid: 2 days in the office, 3 days remote) 📄 B2B Contract: 210 – 260 PLN / hour We are looking for a proactive Senior Data Engineer (Python / GenAI) to join an international team building advanced data integration and retrieval solutions powering Generative AI across the organization. You will work closely with Engineers, Architects and AI Specialists to design, build and optimize data flows that directly support AI agents, RAG pipelines and large‑scale GenAI applications. This role sits within a collaborative engineering environment and offers the opportunity to influence architecture, development standards and the overall quality of next‑generation AI‑driven systems. Key Responsibilities: Design and develop data integration components enabling GenAI models to access and process data from multiple internal systems. Build and maintain data transfer pipelines ensuring scalability, reliability and security. Develop Python‑based microservices (FastAPI) integrated with broader GenAI platforms. Work with vector stores, memory stores and RAG pipelines to optimize data retrieval and context generation for AI agents. Collaborate with cross‑functional teams to align data integrations with overall GenAI architecture. Implement monitoring, observability and performance tuning for data services. Participate in Agile ceremonies: sprint planning, reviews, retrospectives and daily stand‑ups. Requirements: 5+ years of experience in software or data engineering with strong focus on Python . Hands‑on experience with FastAPI , microservices and API‑driven architectures. Experience with databases and data platforms such as MongoDB, PostgreSQL, Hadoop, BigQuery, Starburst or similar. Understanding of vector stores , embeddings or semantic search concepts. Experience working with cloud platforms (GCP or Azure). Solid understanding of Unix/Linux environments. Strong debugging skills and ability to clearly communicate findings. Nice to Have: Experience with RAG pipelines , context engineering or evaluation systems. Knowledge of Google ADK or similar AI development toolkits. Experience with memory stores or caching systems. Exposure to GenAI platforms, LLM‑based solutions or AI agents. Familiarity with DevOps practices and CI/CD pipelines. What We Offer: Long‑term cooperation within a high‑impact, innovative AI programme. Real influence on architecture, engineering standards and data integration strategy. Work in a multicultural, distributed environment. Private medical care (LuxMed) and Multisport card. Stable B2B contract with competitive hourly rate. To learn more about Antal, please visit www.antal.pl

🏢 Summary: Full-time remote B2B role for a Software Engineer focused on Python backend development and implementation of Generative AI solutions for internal products. The position centers on integrating LLM-based models via APIs, building enterprise-grade microservices, and collaborating with Data Scientists in a distributed Agile team. Work involves productionizing AI models within the Azure ecosystem, with emphasis on structured, secure, and high-quality implementations. 🗂️ Requirements: Strong experience in Python backend development, Experience implementing LLM solutions via APIs, Understanding of Generative AI architectures and concepts, Experience in enterprise-scale environments, Experience building and consuming APIs, Ability to work in regulated or structured environments, English proficiency at C1 or strong B2 level 📃 Skills: Python, LLM, GenAI, APIs, RAG, Azure, AzureOpenAI, Microservices, REST, VectorDB 🏢 Description: PROJECT DETAILS: Workload: Full-time Model of working: 100% Remote Contract: B2B, long-term Rate: up to 145 PLN/h nett + VAT About the Role We are looking for a Software Engineer to work on Generative AI–based internal products developed in the company, supporting internal employees. You will join a distributed, fully remote team working on one of two AI-related business streams. The focus of the role is on Python backend development combined with implementation of Generative AI solutions , closely collaborating with Data Scientists who prepare the models. The solutions you build will be used internally within the organization. Responsibilities Design, develop, and deploy backend solutions in Python Implement Generative AI models prepared by Data Scientists into production-ready applications Wrap AI models with application logic and expose them as ready-to-use internal solutions Work mainly with API-based integrations , including LLM usage via APIs Implement and support LLM-based solutions (classic LLM usage through APIs) Work with Retrieval-Augmented Generation (RAG) concepts, including knowledge bases for language models Develop and maintain APIs and microservices Ensure solutions meet enterprise-level quality, security, and governance standards Write clean, maintainable code with appropriate test coverage Collaborate within Agile teams (PM, PO, developers, QA, architects) Participate in the full lifecycle: Business analysis Development Hypercare Further development Technology & Architecture Context Backend and GenAI work is fully Python-based GenAI models are consumed and exposed via APIs Azure OpenAI is used as part of the Azure ecosystem Strong emphasis on correct and well-structured implementation of AI models , not on model training Approximate work split: 80% backend Python development 20% GenAI/LLM solution implementation Key Requirements Strong experience in Python backend development Experience implementing solutions using LLMs via API Understanding of Generative AI architectures and basic GenAI concepts Experience with enterprise-scale environments Experience in building and consuming APIs Ability to work in regulated or structured organizational environments English: C1 or strong B2 Nice to Have Experience in regulated industries Familiarity with RAG concepts (vector databases / knowledge bases) Experience working in large enterprise organizations

🏢 Summary: Full-time remote B2B role for a Software Engineer focused on Python backend development and implementation of Generative AI solutions for internal enterprise products. The position involves integrating LLM-based models via APIs, building microservices, and deploying production-ready AI applications in an Azure-based environment. Work is primarily backend development with partial focus on GenAI/LLM integration in a distributed Agile team. 🗂️ Requirements: Strong experience in Python backend development, Experience implementing LLM solutions via APIs, Understanding of Generative AI architectures and concepts, Experience building and consuming APIs, Experience with enterprise-scale systems, Ability to work in regulated or structured environments, English proficiency at B2 or higher 📃 Skills: Python, GenAI, LLM, API, Azure, AzureOpenAI, RAG, Microservices, REST, VectorDB, Git, Agile 🏢 Description: PROJECT DETAILS: Workload: Full-time Model of working: 100% Remote Contract: B2B, long-term Rate: up to 145 PLN/h nett + VAT About the Role We are looking for a Software Engineer to work on Generative AI–based internal products developed in the company, supporting internal employees. You will join a distributed, fully remote team working on one of two AI-related business streams. The focus of the role is on Python backend development combined with implementation of Generative AI solutions , closely collaborating with Data Scientists who prepare the models. The solutions you build will be used internally within the organization. Responsibilities Design, develop, and deploy backend solutions in Python Implement Generative AI models prepared by Data Scientists into production-ready applications Wrap AI models with application logic and expose them as ready-to-use internal solutions Work mainly with API-based integrations , including LLM usage via APIs Implement and support LLM-based solutions (classic LLM usage through APIs) Work with Retrieval-Augmented Generation (RAG) concepts, including knowledge bases for language models Develop and maintain APIs and microservices Ensure solutions meet enterprise-level quality, security, and governance standards Write clean, maintainable code with appropriate test coverage Collaborate within Agile teams (PM, PO, developers, QA, architects) Participate in the full lifecycle: Business analysis Development Hypercare Further development Technology & Architecture Context Backend and GenAI work is fully Python-based GenAI models are consumed and exposed via APIs Azure OpenAI is used as part of the Azure ecosystem Strong emphasis on correct and well-structured implementation of AI models , not on model training Approximate work split: 80% backend Python development 20% GenAI/LLM solution implementation Key Requirements Strong experience in Python backend development Experience implementing solutions using LLMs via API Understanding of Generative AI architectures and basic GenAI concepts Experience with enterprise-scale environments Experience in building and consuming APIs Ability to work in regulated or structured organizational environments English: C1 or strong B2 Nice to Have Experience in regulated industries Familiarity with RAG concepts (vector databases / knowledge bases) Experience working in large enterprise organizations

🏢 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: Full-time remote Senior AI Engineer role focused on designing and operating production-grade GenAI systems, including agentic workflows and RAG pipelines integrated into enterprise platforms. The position emphasizes backend engineering, API integrations, and orchestration of LLM capabilities in secure, scalable cloud environments rather than ML research. The role also includes mentoring engineers and establishing best practices for reliable, observable AI services. 🗂️ Requirements: 5+ years professional software development experience, Proficiency in Python, Experience building production LLM applications, Hands-on experience with RAG systems, Experience designing and integrating RESTful APIs, Strong understanding of distributed systems fundamentals, Experience with SQL databases and performance optimization, Experience with vector search, Experience integrating external services via APIs, Knowledge of Azure cloud services, Experience with unit and integration testing, Ability to build AI evaluation and testing frameworks 📃 Skills: Python, REST, GraphQL, SQL, Azure, RAG, LLM, LangChain, LangGraph, ASP.NET, APIs, Microservices, DistributedSystems, Concurrency, AsyncIO, VectorSearch, Embeddings, Caching, AzureFunctions, Storage, KeyVault, AppConfiguration, ApplicationInsights, ServiceBus, EventHubs 🏢 Description: Workload: full-time Work model: 100% Remote We are seeking a Senior AI Engineer who combines strong software engineering fundamentals with hands-on experience building production GenAI solutions, including agentic workflows and Retrieval-Augmented Generation (RAG). This is an engineering and orchestration role focused on integrating LLM capabilities into enterprise systems – not a traditional model-training/ML research role. 5+ years of professional software development experience (ideally 7+ years across backend/API/integration and cloud platforms). Proven ability to ship production-grade LLM applications ( RAG , tool/function calling, agent orchestration) with reliability, security, and observability. Strong ownership mindset and passion for AI engineering – curiosity, experimentation, and a drive to continuously improve the product and the team. Excellent communication and collaboration skills; ability to guide, mentor, and unblock other engineers as we build out an AI engineering capability. Main Responsibilities: Design, build, and operate agentic AI services that orchestrate tools, workflows, and integrations across cloud systems and enterprise data sources. Implement and continuously improve RAG pipelines for tax artifacts and internal knowledge, including ingestion, retrieval tuning, and evaluation. Integrate AI workflows with existing internal platforms (e.g., assistant frameworks) and back-end services through robust APIs. Define and maintain tool/function schemas and orchestration patterns; implement streaming updates, interrupts, and human-in-the-loop steps as needed. Partner with other engineers to set direction, mentor, and unblock the team — helping establish strong foundations for the AI initiative. Build in quality from day one: automated tests, evaluation checks, monitoring/telemetry, and performance optimization for network-bound workloads. Participate in Agile ceremonies (daily scrums, refinement/grooming, planning) and collaborate through peer review, pair programming, and strong documentation. Apply best practices, design principles, and security standards throughout the SDLC, with a focus on reliability and responsible AI. Key Requirements: Strong software engineering background (not a research-only data science profile): designing, building, and operating production systems. Proficiency in at least one backend language used for AI systems (Python preferred). Hands-on experience building and integrating RESTful APIs ; GraphQL experience is a plus. Strong understanding of distributed systems fundamentals : concurrency, async I/O, resiliency/retries, rate limits, caching, and performance optimization. Experience integrating with external services and internal platforms via APIs and event-driven patterns. Solid database fundamentals (SQL design, performance, migrations); experience with vector search is required, and hybrid search stores are a plus. Hands-on experience building LLM-powered applications end-to-end : prompt design, tool/function interfaces, structured outputs, and streaming user experiences. Experience with RAG systems : document ingestion pipelines, chunking/metadata, embeddings, retrieval strategies, grounding, and evaluation. Cloud services expertise : Strong knowledge of Azure cloud services used for enterprise AI solutions (e.g., Functions, Storage, Key Vault, App Configuration, Application Insights). Development practices experience : Strong background in unit and integration testing; ability to build and maintain AI evaluation harnesses (golden sets, regression tests, automated checks). Nice to Have: Direct experience with LangGraph and/or LangChain for multi-agent workflows. Familiarity with emerging agentic ecosystem concepts/protocols (e.g., MCP, A2A, ADK or similar). Experience integrating AI services into .NET (ASP.NET Core) applications or building AI microservices that serve enterprise applications. Experience with event-driven architectures (service bus, event hubs) and real-time updates/streaming to UI. Experience working with tax/enterprise document corpora and governance constraints (PII, retention, access control). Other Details: This position is designed for remote work and has a flexible duration, allowing for innovation in the AI space within an agile environment.

🏢 Summary: Full-time remote Senior AI Engineer role focused on designing and operating production-grade GenAI solutions, including agentic workflows and RAG pipelines integrated into enterprise systems. The position emphasizes software engineering excellence, cloud-based AI services, and orchestration of LLM capabilities rather than model research. You will build reliable, secure, and observable AI services integrated with internal platforms and APIs. 🗂️ Requirements: 5+ years of professional software development experience, Strong background in designing and operating production systems, Proficiency in Python or another backend language for AI systems, Experience building and integrating RESTful APIs, Understanding of distributed systems fundamentals, Experience with API integrations and event-driven architectures, Solid SQL and database design knowledge, Experience with vector search, Hands-on experience building end-to-end LLM applications, Experience implementing RAG pipelines, Strong knowledge of Azure cloud services, Experience with unit and integration testing, Ability to build AI evaluation and regression testing frameworks 📃 Skills: Python, REST, GraphQL, SQL, Azure, RAG, LLM, LangChain, LangGraph, ASP.NET, APIs, Embeddings, VectorSearch, AzureFunctions, AzureStorage, KeyVault, AppConfiguration, ApplicationInsights 🏢 Description: Workload: full-time Work model: 100% Remote We are seeking a Senior AI Engineer who combines strong software engineering fundamentals with hands-on experience building production GenAI solutions, including agentic workflows and Retrieval-Augmented Generation (RAG). This is an engineering and orchestration role focused on integrating LLM capabilities into enterprise systems – not a traditional model-training/ML research role. 5+ years of professional software development experience (ideally 7+ years across backend/API/integration and cloud platforms). Proven ability to ship production-grade LLM applications ( RAG , tool/function calling, agent orchestration) with reliability, security, and observability. Strong ownership mindset and passion for AI engineering – curiosity, experimentation, and a drive to continuously improve the product and the team. Excellent communication and collaboration skills; ability to guide, mentor, and unblock other engineers as we build out an AI engineering capability. Main Responsibilities: Design, build, and operate agentic AI services that orchestrate tools, workflows, and integrations across cloud systems and enterprise data sources. Implement and continuously improve RAG pipelines for tax artifacts and internal knowledge, including ingestion, retrieval tuning, and evaluation. Integrate AI workflows with existing internal platforms (e.g., assistant frameworks) and back-end services through robust APIs. Define and maintain tool/function schemas and orchestration patterns; implement streaming updates, interrupts, and human-in-the-loop steps as needed. Partner with other engineers to set direction, mentor, and unblock the team — helping establish strong foundations for the AI initiative. Build in quality from day one: automated tests, evaluation checks, monitoring/telemetry, and performance optimization for network-bound workloads. Participate in Agile ceremonies (daily scrums, refinement/grooming, planning) and collaborate through peer review, pair programming, and strong documentation. Apply best practices, design principles, and security standards throughout the SDLC, with a focus on reliability and responsible AI. Key Requirements: Strong software engineering background (not a research-only data science profile): designing, building, and operating production systems. Proficiency in at least one backend language used for AI systems (Python preferred). Hands-on experience building and integrating RESTful APIs ; GraphQL experience is a plus. Strong understanding of distributed systems fundamentals : concurrency, async I/O, resiliency/retries, rate limits, caching, and performance optimization. Experience integrating with external services and internal platforms via APIs and event-driven patterns. Solid database fundamentals (SQL design, performance, migrations); experience with vector search is required, and hybrid search stores are a plus. Hands-on experience building LLM-powered applications end-to-end : prompt design, tool/function interfaces, structured outputs, and streaming user experiences. Experience with RAG systems : document ingestion pipelines, chunking/metadata, embeddings, retrieval strategies, grounding, and evaluation. Cloud services expertise : Strong knowledge of Azure cloud services used for enterprise AI solutions (e.g., Functions, Storage, Key Vault, App Configuration, Application Insights). Development practices experience : Strong background in unit and integration testing; ability to build and maintain AI evaluation harnesses (golden sets, regression tests, automated checks). Nice to Have: Direct experience with LangGraph and/or LangChain for multi-agent workflows. Familiarity with emerging agentic ecosystem concepts/protocols (e.g., MCP, A2A, ADK or similar). Experience integrating AI services into .NET (ASP.NET Core) applications or building AI microservices that serve enterprise applications. Experience with event-driven architectures (service bus, event hubs) and real-time updates/streaming to UI. Experience working with tax/enterprise document corpora and governance constraints (PII, retention, access control). Other Details: This position is designed for remote work and has a flexible duration, allowing for innovation in the AI space within an agile environment.