AI Engineer II / III

Requirements: Strong programming skills in Python.Extensive hands-on experience in Large Language Model (LLM) development.Strong understanding of Natural Language Processing (NLP) fundamentals.Deep knowledge of Transformer architectures.Hands-on experience with model building, fine-tuning, optimisation, and deployment.Strong computer science fundamentals, including algorithms, distributed systems, and software engineering.Strong machine learning fundamentals.Graduate from top engineering institutes (IITs, IISc, IIITs, BITS, and top NITs). Should Have: Advanced knowledge of modern large language models and generative AI.Experience building and managing AI infrastructure for large-scale model training and inference.Strong expertise in AI evaluation frameworks, model benchmarking, and quality measurement.Experience with production AI systems operating at scale. Could Have: Published research papers or patents in AI, machine learning, or NLP.Active contributions to open-source AI/ML projects.Strong Kaggle rankings or competitive machine learning achievements.Experience training or building foundation models.Reinforcement Learning (RL) or Reinforcement Learning from Human Feedback (RLHF) experience.Experience with Vision Language Models (VLMs).Experience building speech AI systems.Experience with multimodal AI architectures.Domain expertise in healthcare AI.Experience working with knowledge graphs.Experience serving billion-token or high-scale inference workloads. Requirements: Strong programming skills in Python.Extensive hands-on experience in Large Language Model (LLM) development.Strong understanding of Natural Language Processing (NLP) fundamentals.Deep knowledge of Transformer architectures.Hands-on experience with model building, fine-tuning, optimisation, and deployment.Strong computer science fundamentals, including algorithms, distributed systems, and software engineering.Strong machine learning fundamentals.Graduate from top engineering institutes (IITs, IISc, IIITs, BITS, and top NITs). Should Have: Advanced knowledge of modern large language models and generative AI.Experience building and managing AI infrastructure for large-scale model training and inference.Strong expertise in AI evaluation frameworks, model benchmarking, and quality measurement.Experience with production AI systems operating at scale. Could Have: Published research papers or patents in AI, machine learning, or NLP.Active contributions to open-source AI/ML projects.Strong Kaggle rankings or competitive machine learning achievements.Experience training or building foundation models.Reinforcement Learning (RL) or Reinforcement Learning from Human Feedback (RLHF) experience.Experience with Vision Language Models (VLMs).Experience building speech AI systems.Experience with multimodal AI architectures.Domain expertise in healthcare AI.Experience working with knowledge graphs.Experience serving billion-token or high-scale inference workloads.