GenAI Engineer

• Design, develop, and deploy generative AI usecases using LLMs and pipelines into production environments.

• Build and manage MLOps platforms tailored for generative workflows, including fine-tuning, and monitoring of generative AI models.

• Create scalable, distributed systems to support real-time generative applications.

• Implement prompt engineering and retrieval-augmented generation (RAG) techniques to optimize model outputs. Develop cloud-native applications using frameworks like TensorFlow, PyTorch, LangChain, LangGraph and Hugging Face for generative tasks.

• Optimize low-latency, high-throughput systems for deploying GenAI models in production.

• Ensure ethical AI practices, including bias mitigation, model interpretability, and compliance with data privacy standards.

• Collaborate with cross-functional teams to integrate GenAI solutions into products and services.

Qualifications:

• Bachelor’s/Master’s degree in Computer Science, AI, or related field.

• 1 -3+ years of experience in Data Science, generative AI, including LLMs, NLP, or multi-modal models.

• Proficiency in Python and experience with GenAI libraries (e.g., Transformers, PyTorch, LangChain, LangGraph).

• Strong understanding of distributed systems and scalable architectures for GenAI workloads.

• Familiarity with vector databases and retrieval systems for RAG pipelines.

• Experience with prompt engineering, fine-tuning, and model optimization (e.g., quantization, pruning). Knowledge of CI/CD pipelines for GenAI workflows and version control (e.g., Git, DVC).

• Understanding of ethical AI principles and tools for bias detection/monitoring.

• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.