Sr Artificial Intelligence/Machine Learning Engineer

Job Summary

Ciklum is looking for a Senior Artificial Intelligence/Machine Learning Engineer to join our team full-time in India.

As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.

Responsibilities

• System Architecture: Lead the end-to-end design and deployment of scalable AI systems, specifically focusing on LLM-driven applications, Multi-Agent systems, and Retrieval-Augmented Generation (RAG)
• Technical Governance: Serve as the primary technical authority for clients, ensuring all AI solutions meet enterprise-grade standards for performance, security, and reliability
• Hands-on Development: Drive the full development lifecycle from initial experimentation and rapid prototyping to production-grade implementation
• Strategic Presales: Act as a Technical SME during discovery phases, translating business challenges into viable AI roadmaps and engineering solutions
• Operational Excellence (LLMOps): Establish best practices for CI/CD, model observability, and automated evaluation to ensure continuous improvement of deployed models
• Cross-Functional Leadership: Collaborate with Product and Data teams to align technical delivery with business KPIs, while mentoring junior engineers through code reviews and internal workshops

Requirements

• Professional Background: 8+ years in software, data, or AI engineering, featuring at least 3-4 years of direct experience architecting and implementing AI/ML solutions
• Academic Foundation: BSc, MSc, or PhD in Computer Science, Mathematics, or a related quantitative field, with a deep command of probability, statistics, and machine learning optimization
• AI Generative AI Expertise: Proven track record in building and deploying advanced AI systems, including Large Language Models (LLMs), Multimodal architectures, RAG, and Agentic systems
• Engineering Proficiency: Expert-level Python skills and mastery of frameworks such as PyTorch, TensorFlow, LangChain, and Hugging Face
• Cloud Infrastructure: Hands-on experience with cloud-native AI stacks (AWS SageMaker, Azure ML, or GCP Vertex AI) and enterprise data platforms
• Operational Excellence (LLMOps): Proficiency in modern AI engineering practices, including CI/CD, model versioning, observability, and evaluation methodologies (e.g. CRISP-ML(Q))
• Full-Stack AI Delivery: Experience architecting end-to-end pipelines-from data ingestion and API integration to production-grade model serving and optimization
• Data Sophistication: Ability to handle diverse modalities (text, image, audio) and complex scenarios such as time-series forecasting and anomaly detection
• Governance Security: Strong understanding of security, data privacy (GDPR/CCPA), and ethical AI frameworks within enterprise system design

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