Principal AI Lead

Principal AI Lead

About the role:

Principal AI Lead

Responsibilities:

• 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

Commercial / Presales:

• Drive technical leadership in presales engagements, shaping AI strategies and architectures that align with client business objectives
• Collaborate directly with C-suite and senior client executives to define AI roadmaps, value propositions, and investment priorities
• Produce high-quality technical content for proposals, RFPs, whitepapers, and thought- leadership materials
• Partner with delivery and commercial teams to identify new growth opportunities and establish Ciklum as a trusted AI partner

Leadership & Practice Development:

• Provide strategic and technical leadership to AI engineering teams across multiple projects and geographies
• Shape hiring strategy and talent development plans to scale AI capabilities within Ciklum
• Mentor, coach and review work of Expert-level engineers, ensuring consistent delivery quality and architectural rigor
• Contribute to the definition and rollout of AI practice-wide standards, playbooks, and reusable components

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:

• 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

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:

• 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)

Business-related requirements:

• 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

Desirable:

• Track record of AI thought leadership through publications, blogs, podcasts or open- source contributions
• Recognized presence in professional or academic AI communities (e.g., conferences, research collaborations)
• Experience building and scaling internal AI centers of excellence or practice communities
• 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

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