Ciklum is looking for a Senior Artificial Intelligence/Machine Learning Engineer to join our team full-time in Poland. We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live. About the role: As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-functional development team engineering experiences of tomorrow. Together, we will work on the project focused on designing, deploying, and scaling advanced machine learning infrastructure, building real-time and batch inference systems, and optimizing predictive platforms for efficiency, reliability, and performance. Responsibilities: - Mentor and guide mid-level Machine Learning Engineers, providing technical leadership and shaping best practices across the organisation - Conduct comprehensive code reviews, identify areas for improvement, and foster a culture of continuous learning and collaboration - Communicate complex machine learning concepts effectively to both technical and non-technical stakeholders - Translate business requirements into actionable ML solutions and present innovative proposals to key stakeholders - Lead the integration of new technologies and tools into the ML infrastructure to stay ahead of industry trends - Architect and oversee the development of complex machine learning pipelines for real-time and batch inference - Collaborate closely with Data Science teams to ensure models are optimized and deployed efficiently in production Requirements: - Expert-level proficiency with cloud technologies (ideally AWS) and extensive experience with containerization and orchestration (preferably Kubernetes) - Deep understanding of software development, DevOps, and MLOps best practices, with a proven track record of applying them in production - Extensive experience in designing, deploying, and maintaining scalable models and services in production environments - Strong understanding of Machine Learning, with the ability to collaborate deeply with Data Scientists on model deployment and optimization - Hands-on experience with Generative AI, LLMs, and/or NLP solutions in production environments - Experience with model explainability techniques (e.g., SHAP, Grad-CAM) and understanding of model governance, risk management, and compliance requirements - Experience with A/B testing, experimentation frameworks, and evaluating ML models against business KPIs - Experience with MLflow for experiment tracking and model management, and Airflow or Dagster for orchestrating end-to-end ML pipelines and workflows - Significant experience with Data Engineering, Kafka, and stream processing - Proficiency in Python and SQL; or any other programming languages is a strong plus What`s in it for you? - Strong community: Work alongside top professionals in a friendly, open-door environment - Growth focus: Take on large-scale projects with a global impact and expand your expertise - Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications - Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands-on experience with cutting-edge technologies - Flexibility: Enjoy flexibility – full remote working possibilities - Care: We’ve got you covered with company-paid medical insurance, mental health support, and financial & legal consultations About us: At Ciklum, we are always exploring innovations, empowering each other to achieve more, and engineering solutions that matter. With us, you’ll work with cutting-edge technologies, contribute to impactful projects, and be part of a One Team culture that values collaboration and progress. With delivery centers in Wrocław and Gdańsk, our 300+ professionals in Poland drive forward-thinking solutions for global clients. Join a community where collaboration sparks innovation—and your impact reaches millions. Want to learn more about us? Follow us on Instagram, Facebook, LinkedIn. Explore, empower, engineer with Ciklum! Interested already? We would love to get to know you! Submit your application. We can’t wait to see you at Ciklum.

• Lead end-to-end machine learning initiatives focused on improving player engagement and retention, from initial concept through production deployment. • Build scalable, reusable machine learning pipelines with a focus on reliability, maintainability, and performance. • Design and manage CI/CD workflows for machine learning using tools like MLflow, Jenkins, and GitOps to enable automated and efficient model deployment. • Monitor model performance in production, implementing retraining strategies, drift detection, and continuous optimization. • Partner with cross-functional teams to translate business goals and user insights into high-impact machine learning solutions. • Mentor other engineers and help define best practices for machine learning system design, development, and deployment.

• Build and productionize forecasting models that capture the overall trajectory of Netflix's subscription business alongside the expected impact of pricing actions on member base, ARPU, and revenue across global markets • Build on the team's existing scenario modeling and simulation tools to quantify how pricing actions and broader market dynamics are expected to affect subscription metrics - giving decision-makers a way to stress-test assumptions before a pricing move happens • Contribute to and advance the team's forecasting pipeline - model development, backtesting, deployment, and monitoring - and lift the accuracy and reliability of existing forecasts • Partner with Finance & Strategy and Product leadership to translate forecast outputs - including their uncertainty - into concrete business recommendations

• Design and implement quasi-experimental and causal inference approaches • Build and productionize measurement models and causal inference pipelines • Conduct elasticity and willingness-to-pay research • Evolve measurement and analytics tools • Partner with Finance & Strategy and Product leadership to translate statistical findings into business recommendations • Own work from ideation to production systems