Expert AI Engineer
RomaniaFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- DockerPythonSQLJavaKafkaKubernetesPyTorchSnowflakeCassandraSparkTensorflowScalaDatabricksMLOpsLangChain
Requirements
- 8+ years of professional experience in software, data, or AI engineering
- 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
- Proven experience in architecting and implementing end-to-end AI/ML solutions
- Strong software engineering skills for AI system development (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
- 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)
- 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
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)
- 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
- 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
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