Technical Lead AIML

Job Overview:

We are seeking an experienced and visionary Technical Lead – Data Science to lead and drive AI/ML solution delivery across complex and large-scale business problems. The ideal candidate will have deep hands-on expertise in AI, Machine Learning, Deep Learning, NLP, and Generative AI (Gen AI), coupled with a strong leadership mindset to mentor teams, architect scalable solutions, and influence key stakeholders.

Key Responsibilities:

• Lead the architecture, development, and deployment of advanced AI/ML/NLP/Gen AI models and solutions.
• Work closely with business stakeholders to understand use cases, translate them into technical solutions, and drive end-to-end implementation.
• Design and build scalable and production-grade machine learning systems, ensuring high performance, reliability, and efficiency.
• Apply Natural Language Processing (NLP) and Generative AI (LLMs, transformers, prompt engineering) for innovative and high-impact business applications.
• Mentor and lead a team of data scientists and ML engineers, ensuring code quality, knowledge sharing, and best practice adoption.
• Drive technical decision-making, model governance, risk mitigation, and continuous improvement.
• Collaborate with cross-functional teams across data engineering, product, and DevOps to ensure seamless integration and delivery.

Required Skills:

• Strong programming skills in Python and experience with key libraries such as TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers.
• Deep expertise in Machine Learning, NLP, and Generative AI, with proven success in implementing real-world solutions.
• Hands-on experience with cloud platforms like AWS, Azure, or GCP for model training, deployment, and scaling.
• Solid understanding of data structures, algorithms, and MLOps practices.
• Experience in leading medium to large AI/ML teams and delivering high-visibility projects.
• Strong communication and stakeholder management skills, with the ability to influence and present to senior leadership.

Preferred Skills:

• Exposure to end-to-end ML lifecycle including data prep, model monitoring, CI/CD pipelines, and model interpretability tools.
• Experience in product-oriented or consulting organizations is a plus.
• Certifications in AI/ML or cloud technologies (AWS/GCP/Azure) are advantageous.