AI Engineer

This is a fully remote position, open to applicants in Poland.

📋 Description

• Integrate into product teams and collaborate one-on-one with senior engineers on real tasks.

• Co-develop and enhance methods for incorporating AI into everyday engineering processes.

• Assist teams in adopting "agentic" work styles through practical application rather than just guidance.

• Initiate with one developer per team (phased rollout, not all teams simultaneously).

• Primarily concentrate on developers, with the potential to extend support to QA, BA, and DevOps over time.

• Utilize and adapt to the sanctioned internal toolset (e.g., Kiro, potentially Claude), ensuring adherence to TUI standards.

• Collaborate with internal AI/innovation teams to identify tooling gaps or opportunities for improvement.

• What Success Looks Like:

• Engineers are actively integrating AI into their daily responsibilities in a meaningful manner.

• AI is embedded in actual development tasks (not merely experimentation or training).

• Teams experience increased efficiency through practical AI integration.

• Clear, reusable patterns for AI-supported development begin to surface.

⛳️ Requirements

• Over 8 years of professional experience in software, data, or AI engineering, with at least 3–4 years of hands-on experience in designing and implementing AI/ML solutions.

• BSc, MSc, or PhD in Computer Science, Mathematics, Engineering, or a related quantitative field.

• Comprehensive understanding of probability, statistics, and the mathematical foundations of machine learning and optimization.

• Demonstrated experience in building and deploying advanced AI systems, including Large Language Models (LLMs), multimodal, and generative AI architectures.

• Familiarity with agentic system design, retrieval-augmented generation (RAG), and prompt engineering techniques.

• Strong proficiency in Python and experience with AI/ML development frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face, or equivalents), with the understanding that production environments may also utilize Java and/or Node.js, depending on the team's technology stack.

• Knowledge of both AI development tooling and backend/service-side technologies is advantageous, as the role may involve model development and integration into existing systems.

• Solid grasp of modern AI engineering practices, including model lifecycle management, observability, evaluation, versioning, and continuous improvement.

• Familiarity with AI solution delivery methodologies (e.g., CRISP-ML(Q), TDSP, or modern agile ML lifecycles).

• Ability to visualize, interpret, and effectively communicate model outputs and insights using contemporary tools and dashboards.

• Proven experience in architecting and implementing end-to-end AI/ML solutions—from data ingestion and model training to deployment, monitoring, and optimization.

• Strong software engineering capabilities for AI system development, including data processing, API integration, and model serving (Python, SQL, and optionally Java/Scala or similar).

• Hands-on experience with cloud-native AI platforms and services (AWS SageMaker, Azure ML, GCP Vertex AI, or NVIDIA AI stack)—with AWS as the primary.

• Proficiency in designing scalable ML/LLM pipelines and applying MLOps/LLMOps best practices (CI/CD, orchestration, monitoring, versioning, and deployment automation).

• Experience with various data modalities (structured, text, image, audio, video) and multimodal model integration.

• Familiarity with addressing complex data scenarios such as class imbalance, time-series forecasting, and anomaly detection.

• Understanding security, data governance, and compliance considerations in AI system design.

• Broad exposure to enterprise-scale AI solution design across industries such as BFSI, Healthcare, Aerospace, Manufacturing, Energy, Telecom, or Technology sectors.

• Proven ability to convert business and operational requirements into robust AI system architectures that deliver measurable impact.

• Familiarity with the challenges of deploying AI in regulated environments and ensuring compliance with data privacy and protection frameworks (e.g., GDPR, CCPA, PCI, DSS).

• Experience managing sensitive or high-value data (PII, PHI) and implementing stringent security, governance, and access control mechanisms.

• Understanding of enterprise data ecosystems and integration patterns (CRM, ERP, knowledge management, or workflow systems).

• Proven experience in delivering production-grade AI solutions that result in measurable business and operational outcomes.

• Strong ownership of the complete AI engineering lifecycle—from problem framing and architecture design to deployment, optimization, and continuous improvement.

• Ability to align technical decisions with business priorities, ensuring scalability, reliability, and measurable value from AI initiatives.

• Excellent collaboration and communication skills to effectively engage with cross-functional stakeholders, delivery teams, and clients.

• High degree of autonomy, accountability, and attention to detail in managing complex, multi-component AI systems.

🏝️ Benefits

• Strong community: Collaborate with top professionals in a friendly, open-door environment.

• Growth focus: Engage in large-scale projects with a global impact while expanding your expertise.

• Tailored learning: Enhance your skills through internal events (meetups, conferences, workshops), Udemy access, language courses, and company-sponsored certifications.

• Endless opportunities: Explore various domains through internal mobility, discovering the best fit to gain hands-on experience with cutting-edge technologies.

• Flexibility: Benefit from flexible working arrangements, including full remote work possibilities.

• Care: Enjoy comprehensive coverage with company-paid medical insurance.