Lead AI/ML Engineer
First seen by Alion on Oct 29, 2026.
About the Role :
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.
Skills
Artificial Intelligence, Machine Learning, Python, Deep Learning, Generative AI, PyTorch, Tensorflow, MLOps, NLP, Computer Vision, Prompt Engineering