Description

Design, build, and deploy production-grade ML and GenAI solutions, owning the full lifecycle from concept to operability.

This role is on-site.

Responsibilities

• Lead the architecture and implementation of ML/LLM/GenAI systems, ensuring scalability and reliability in production.
• Build and maintain MLOps pipelines for automated deployment, monitoring, and model lifecycle management.
• Fine-tune generative models for NLP tasks, optimizing performance through rigorous evaluation and iteration.
• Mentor junior engineers via design/code reviews and pairing, raising overall engineering quality.
• Communicate technical results to stakeholders and stay current with AI research advancements.

Required Skills

• 10+ years of engineering experience, including 3-5+ years in production AI/ML systems.
• Strong proficiency in Python for model development and integration.
• Hands-on experience with TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
• Deep understanding of MLOps, model monitoring, and governance.
• Experience with AWS, Azure, or Google Cloud Platform.
• Proficiency with Docker, Kubernetes, and microservices design.
• Solid grasp of statistics, deep learning, and generative architectures (GANs, VAEs).

Preferred Skills

• Bachelor's or Master's degree in Computer Science or Engineering.
• Portfolio of successful GenAI/NLP applications using OpenAI APIs.
• Experience with prompt engineering and external API integration.

Key Skills

Education

Bachelor's or Master's degrees