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Ciklum

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Artificial Intelligence • B2B • Enterprise

Ciklum is a global digital engineering and AI-enabled product and platform services company that helps enterprises design, build, and scale AI-infused software, cloud, data, and automation solutions. It combines UX and product design with engineering, DevOps, data engineering, responsible AI, and edge/IoT capabilities to move pilots into production and deliver enterprise-ready outcomes across industries such as banking, retail, healthcare, hi-tech, automotive, and travel. Ciklum emphasizes platform-agnostic, scalable solutions—covering AI incubators, conversational AI, agentic automation, cloud and edge services, XR/AR/VR, and digital assurance—focused on transforming workflows and customer experiences for B2B enterprise clients.

1001 - 5000 employees

Founded 2002

🤖 Artificial Intelligence

🤝 B2B

🏢 Enterprise

📋 Description

• Define and evolve Ciklum’s AI Engineering strategy, frameworks and best practices across clients and internal initiatives • Serve as the primary technical advisor to executive stakeholders (CTOs, CIOs, VPs of Engineering) on AI strategy and system architecture • Drive the vision and roadmap for enterprise AI adoption, guiding teams on architecture, governance, and value realization • 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 • Act as the overall AI architecture authority across multiple engagements, ensuring design excellence, scalability and alignment with client enterprise standards • 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 • Champion innovation in AI through the creation of reusable frameworks, accelerators, and reference architectures • Evangelize AI best practices internally and externally through publications, talks, and open-source contributions • Guide client and internal teams in adopting emerging AI paradigms such as agentic systems, RAG frameworks and autonomous orchestration • Lead and mentor senior AI engineers, fostering a culture of excellence, experimentation and continuous learning across teams and practices (e.g., contributing to Ciklum’s AI Academy) • Represent Ciklum’s AI capability in public forums, conferences, and strategic client discussions • Collaborate with marketing and partnerships teams to strengthen Ciklum’s brand as an AI thought leader

🎯 Requirements

• 10+ years of professional experience in software or data engineering, including at least 4–5 years in AI/ML solution architecture or AI leadership roles • Demonstrated experience leading cross-functional AI programs or large-scale AI solution deliveries • 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 architecting and scaling enterprise-grade AI systems, including LLM-driven and multimodal solutions, across production environments • Experience defining AI system blueprints, reference architectures, and integration patterns across multiple domains and client ecosystems • 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 — 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 • Ability to advise senior stakeholders on AI opportunity assessment, ROI modeling and transformation strategies • Experience designing AI governance frameworks and compliance strategies in highly regulated industries • 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) • Strong executive communication and storytelling ability, capable of influencing technical and business audiences alike • Proven record of representing organizations in external speaking engagements, conferences, or public panels • Demonstrated experience driving innovation initiatives and contributing to organizational AI strategy • 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

🏖️ Benefits

• 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