Senior AI Engineer
FractalBengaluru, Karnataka, India
Posted March 23, 2026
Full Time
About the Company
Fractal is one of the most prominent providers of Artificial Intelligence to Fortune 500® companies. Fractal's vision is to power every human decision in the enterprise, and bring AI, engineering, and design to help the world's most admired companies. Fractal's businesses include Crux Intelligence (AI driven business intelligence), Eugenie.ai (AI for sustainability), Asper.ai (AI for revenue growth management) and Senseforth.ai (conversational AI for sales and customer service). Fractal incubated Qure.ai, a leading player in healthcare AI for detecting Tuberculosis and Lung cancer. Fractal currently has 4000+ employees across 16 global locations, including the United States, UK, Ukraine, India, Singapore, and Australia. Fractal has been recognized as 'Great Workplace' and 'India's Best Workplaces for Women' in the top 100 (large) category by The Great Place to Work® Institute; featured as a leader in Customer Analytics Service Providers Wave™ 2021, Computer Vision Consultancies Wave™ 2020 & Specialized Insights Service Providers Wave™ 2020 by Forrester Research Inc., a leader in Analytics & AI Services Specialists Peak Matrix 2022 by Everest Group and recognized as an 'Honorable Vendor' in 2022 Magic Quadrant™ for data & analytics by Gartner Inc. For more information, visit fractal.ai
Job Description
We're looking for a Senior AI Engineer to lead the development, deployment, and operationalization of large language model-based solutions. As the Lead for LLMOps, you will spearhead the team, manage the lifecycle of LLM applications, and ensure scalable, secure, and responsible AI implementations across the organization.
Requirements
- Master's or PhD in Computer Science, Machine Learning, NLP, or related field.
- 8+ years of experience in AI/ML, with at least 2 years in LLMOps or generative AI.
- Proven track record of deploying LLMs in production environments.
- Deep understanding of NLP architectures (Transformers, RNNs, CNNs).
- Experience with LangChain, RAG pipelines, vector databases, and containerized workflows.
- Strong grasp of cloud-native tools and MLOps frameworks.
