Ciklum
Senior Artificial Intelligence/Machine Learning Engineer
- Compensation
- Not disclosed
- Location
- India
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Full time
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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
Full description
Ciklum is looking for a Senior Artificial Intelligence/Machine Learning Engineer to join our team full-time in India.
We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.
About the role:
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))