Manager of Artificial Intelligence

Kody Technolab Limited is seeking a Lead AI/ML Engineer to design, develop, and deploy cutting-edge Artificial Intelligence and Machine Learning solutions. The ideal candidate will have strong expertise in Machine Learning, Deep Learning, Generative AI, LLMs, MLOps, and cloud-based AI deployments.

Key Responsibilities

• Design, develop, and deploy machine learning and deep learning models for classification, regression, recommendation, NLP, computer vision, and/or generative AI tasks.
• Build end-to-end ML pipelines: data preprocessing, model training, validation, evaluation, and deployment.
• Implement state-of-the-art algorithms using frameworks such as PyTorch, TensorFlow, or similar.
• Conduct experiments with LLMs and foundation models (e.g., GPT, BERT, Stable Diffusion, etc.).
• Collaborate with data engineers, product teams, and stakeholders to translate business requirements into ML solutions.
• Optimize model performance and scalability for production environments.
• Stay up-to-date with the latest research and developments in AI/ML/DL/Generative AI.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related field.
• Experience in machine learning, deep learning, or AI product development.
• Proficiency in Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers, etc.
• Experience working with generative models (GANs, VAEs, diffusion models, or LLMs).
• Strong understanding of ML model lifecycle: training, tuning, evaluation, and deployment.
• Experience with MLOps tools (e.g., MLflow, Docker, Kubeflow) is a plus.
• Familiarity with cloud platforms (AWS, Azure, or GCP) is preferred.
• Basic understanding of web development and APIs for integrating ML models into applications.

Nice to Have:

• Experience with prompt engineering or fine-tuning large language models.
• Contributions to open-source AI/ML projects or relevant publications.
• Exposure to data annotation, feature engineering, and model interpretability tools.
• Proficiency in C++ for performance optimization, model deployment, or systems-level programming in AI/ML applications.