Principal AI Lead

Job Description Ciklum is looking for a Principal AI Lead to join our team full-time in India. About the role: As a Principal AI Lead, become a part of a cross-functional development team engineering experiences of tomorrow. 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 45 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 About us: Want to learn more about us Follow us on Instagram, Facebook, LinkedIn. Explore, empower, engineer with Ciklum Interested already We would love to get to know you Submit your application. We can't wait to see you at Ciklum.