ZettaMine - Lead Engineer - Generative AI

Job Description : Generative AI Lead Engineer

Location : Bengaluru, India

Experience : Minimum 5 years in Generative AI

Summary :

We are seeking a highly skilled AI/ML Engineer with strong expertise in Generative AI to join our team in Bengaluru. In this role, you will design, develop, and deploy production-ready AI solutions leveraging advanced deep learning techniques, generative models, and cloud AI services.

You will work closely with cross-functional teams to ensure seamless integration of AI capabilities, optimize performance, and deliver innovative AI-driven applications that enhance business value and user experience.

Roles & Responsibilities :

- Act as a Subject Matter Expert (SME) in Generative AI and related technologies.

- Lead, collaborate, and manage a team to deliver high-quality AI solutions.

- Provide strategic input and solutions to technical challenges within the immediate team and across multiple teams.

- Participate in critical decision-making processes related to AI design, architecture, and deployment.

- Mentor and guide junior team members, fostering skill development in AI/ML technologies.

- Design, train, and fine-tune Generative AI models for real-world applications.

- Continuously evaluate and improve AI systems to ensure scalability, performance, and reliability.

- Collaborate with product managers, architects, and engineering teams to align AI solutions with business objectives.

Professional & Technical Skills :

Must-Have Skills :

- Proven expertise in Generative AI with minimum 5 years of hands-on experience.

- Strong knowledge of deep learning frameworks such as TensorFlow or PyTorch.

- Experience in cloud platforms (AWS, Azure, or GCP) with a focus on AI/ML deployment and optimization.

- Proficiency in data preprocessing, augmentation, and feature engineering.

- Ability to design and implement scalable, production-grade AI solutions.

Good-to-Have Skills :

- Knowledge of LLMs (Large Language Models), diffusion models, or reinforcement learning.

- Hands-on experience with MLOps tools for model deployment, monitoring, and automation.

- Familiarity with APIs, microservices, and containerization (Docker/Kubernetes) for AI integration.

- Strong problem-solving, analytical, and communication skills.