Expert AI Engineer

Expert AI Engineer

Ciklum

Remote, Poland

About the role:

As an Expert AI Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.

Responsibilities:

• Lead the design, development and deployment of advanced AI systems across Data Science and AI Engineering domains
• Architect and implement scalable AI pipelines and LLM-driven applications, including retrieval-augmented generation (RAG), orchestration and multi-agent systems
• Serve as the technical authority during client engagements, ensuring architecture quality, scalability, performance and reliability
• Contribute hands-on to development activities, from experimentation and prototyping to production-grade implementation
• Collaborate with cross-functional engineering, data, and product teams to align technical solutions with client and business objectives
• Apply MLOps and LLMOps best practices for CI/CD, observability, evaluation and continuous improvement of AI models and pipelines
• Integrate AI services with enterprise platforms (e.g., Confluence, Jira, GitHub, CRM, ERP) and ensure seamless interoperability
• Drive innovation through internal frameworks and accelerators
• Ensure all AI solutions comply with security, data privacy and responsible-AI standards
• Mentor engineering peers and support knowledge sharing across teams and practices (e.g., contributing to Ciklum’s AI Academy)

Commercial/Presales:

• Act as technical SME in presales conversations, translating business challenges into viable AI engineering solutions
• Partner with delivery and account teams to shape long-term client engagement strategies based on engineering excellence

Internal resource management/team building and management:

• Support recruitment efforts through technical assessments, interviews, and candidate evaluation
• Mentor team members and contribute to cross-skilling initiatives within the AI and Engineering service lines
• Uphold engineering excellence by promoting best practices, code quality, and delivery standards across projects

Requirements:

We know that sometimes, you can’t tick every box. We would still love to hear from you if you think you’re a good fit!

General technical requirements:

• 8+ years of professional experience in software, data, or AI engineering, including at least 3–4 years of hands-on experience designing and implementing AI/ML solutions
• BSc, MSc, or PhD in Computer Science, Mathematics, Engineering, or a related quantitative field
• Deep understanding of probability, statistics, and the mathematical foundations of machine learning and optimization
• Proven experience building and deploying advanced AI systems, including Large Language Models (LLMs), multimodal, and generative AI architectures
• Exposure to agentic system design, retrieval-augmented generation (RAG) and prompt engineering techniques
• Strong proficiency in Python and common AI/ML development frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face or equivalent)
• Solid understanding 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 communicate model outputs and insights effectively using modern tools and dashboards

Specific technical requirements:

• 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 skills 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)
• Proficiency in designing scalable ML/LLM pipelines and applying MLOps/LLMOps best practices (CI/CD, orchestration, monitoring, versioning, and deployment automation)
• Experience with diverse data modalities (structured, text, image, audio, video) and multimodal model integration
• Familiarity with handling complex data scenarios such as class imbalance, time-series forecasting and anomaly detection
• Understanding of security, data governance and compliance considerations in AI system design

Domain experience:

• Broad exposure to enterprise-scale AI solution design across industries such as BFSI, Healthcare, Aerospace, Manufacturing, Energy, Telecom or Technology sectors
• Proven ability to translate business and operational requirements into robust AI system architectures that deliver measurable impact
• Familiarity with 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), implementing strong security, governance and access control mechanisms
• Understanding of enterprise data ecosystems and integration patterns (CRM, ERP, knowledge management or workflow systems)

Business-related requirements:

• Proven experience delivering production-grade AI solutions that achieve measurable business and operational outcomes
• Strong ownership of the full 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 work effectively with cross-functional stakeholders, delivery teams, and clients
• High degree of autonomy, accountability, and attention to detail in managing complex, multi-component AI systems

Desirable:

• Strong background in software or solution architecture, ideally with previous experience as a Software or Data Architect
• Proven ability to design scalable, distributed, and fault-tolerant AI architectures, integrating APIs, microservices, and event-driven components
• Experience with MLOps and LLMOps practices, including pipeline automation, containerization (Docker, Kubernetes), and continuous deployment of AI models
• Deep learning expertise using TensorFlow, PyTorch, or JAX, including fine-tuning and optimization of large models
• Hands-on experience with Large Language Models (LLMs), Generative AI applications, and agentic or RAG-based systems
• Advanced SQL and familiarity with modern data platforms (Databricks, Snowflake, or equivalent)
• Experience with Big Data and streaming frameworks (Apache Spark, Kafka, Flink, etc.)
• Understanding of NoSQL and graph databases (e.g., Cassandra, Neo4j) and their role in AI knowledge management
• Experience with cloud-native architectures and certified expertise in AWS, Azure, or GCP AI/ML services
• Exposure to research or innovation projects, with publications or open-source contributions considered an advantage