Works directly with generative models, LLMs, and OpenAI/Azure services — closely aligned with building and deploying AI agents and rapid AI prototyping.
About the Role
Senior Gen AI Engineer responsible for preparing data, developing and fine-tuning generative and multimodal models (GANs, VAEs, Transformers/LLMs), and building scalable AI pipelines and services. The role includes deploying AI applications on hyperscaler clouds (Azure, GCP, AWS) and integrating models with company data and services.
Job Description
Role
Senior Gen AI Engineer focused on generative and agentic AI: collecting and preparing data for multimodal foundation models, developing and optimizing generative models (GANs, VAEs, Transformers/LLMs), fine-tuning large language models, and integrating AI capabilities into applications and services.
Key Responsibilities
- Collect, clean, preprocess, augment, and generate synthetic data for training and evaluation of multimodal models.
- Design, develop, and maintain AI pipelines for training, fine-tuning, and evaluating models (including chunking and embeddings workflows).
- Develop backend services in Python or .NET to support OpenAI-powered or other LLM-based solutions.
- Build and deploy AI applications on cloud platforms (Azure, GCP, AWS) and integrate Azure Cognitive Services or equivalent offerings.
- Ensure robustness, efficiency, scalability, and production readiness of AI systems using containerization and orchestration tools.
- Collaborate with cross-functional teams to deploy and integrate AI solutions and stay current with AI/ML advancements.
Requirements
- 6–12 years of relevant experience.
- Strong foundation in machine learning, deep learning, and computer science.
- Expertise in generative AI techniques (e.g., GANs, VAEs) and transformer-based models for language tasks.
- Experience with NLP and optionally computer vision tasks.
- Proficiency with Python (and/or .NET) and AI frameworks such as TensorFlow, PyTorch, and Keras.
- Experience with data management, including cleaning, labeling, augmentation, and synthetic data generation.
- Experience deploying models in production on cloud platforms (Azure, GCP, AWS) and using Docker and Kubernetes for containerization and orchestration.
- Ability to work independently and collaboratively; bring innovative solutions to client engagements.
Interview & Location
- Location: Pan India. Mode of interview: in-person (Coimbatore). Notice period: immediate to 90 days.