Description

You will design, train, and deploy large language models and multimodal agents for enterprise automation.

This role is on-site.

Responsibilities

• Lead the design, training, and deployment of large language models and multimodal agents supporting enterprise automation.
• Develop scalable AI pipelines for real-time inference, retraining, and model monitoring in cloud environments.
• Collaborate with data scientists and platform engineers to translate business use cases into operational AI systems.
• Support prompt engineering, model evaluation, bias detection, and performance tuning for operational reliability.
• Automate deployment, versioning, and monitoring workflows supporting MLOps and responsible AI standards.

Required Skills

• 4+ years of experience in enterprise AI/ML projects, including LLMs, RAG, and multimodal systems.
• Extensive hands-on expertise in Python (3.8+) for training, fine-tuning, and inference of large AI models.
• Proven expertise in PyTorch and TensorFlow for deep learning model development and deployment.
• Experience with LLMs such as GPT, Claude, Llama, or Gemini, including prompt engineering and fine-tuning.
• Proficiency with Hugging Face Transformers, LangChain, and RAG architecture integration.
• Experience deploying and managing scalable AI models on AWS, Azure, or GCP.
• Knowledge of container orchestration using Docker and Kubernetes for cloud-native AI systems.
• Experience with model lifecycle management tools like MLflow or Kubeflow.

Preferred Skills

• Experience with multi-modal processing frameworks for text, images, and audio inputs.
• Familiarity with data processing libraries like Pandas and NumPy for feature engineering.
• Experience with model evaluation and bias mitigation tools for fairness assessment.

Key Skills

Education

Bachelor's or Master's degrees