GenAI Engineer
Summary
Designs, fine-tunes, and deploys generative AI models (LLMs) for production, integrating them into applications using frameworks like PyTorch, TensorFlow, and LangChain.
We are looking for a Generative AI Engineer with 3+ years of hands-on experience in building AI-driven applications. The ideal candidate will have strong expertise in machine learning, deep learning, and large language models (LLMs), with a passion for applying GenAI to solve real-world problems. You will collaborate with product, data science, and engineering teams to design, fine-tune, and deploy generative AI models at scale.
Key Responsibilities
Design, fine-tune, and deploy LLMs and other generative AI models for production use cases.
Work with transformer architectures (GPT, BERT, LLaMA, etc.) for text, image, or multimodal tasks.
Build end-to-end pipelines for training, inference, and evaluation of AI models.
Implement prompt engineering, RAG (Retrieval-Augmented Generation), and model optimization for improved performance.
Integrate GenAI capabilities into web, mobile, or enterprise applications.
Leverage frameworks such as LangChain, Hugging Face, TensorFlow, PyTorch.
Collaborate with backend/frontend teams to develop APIs and services that serve AI models.
Ensure AI solutions meet scalability, latency, and security requirements.
Research and stay updated on the latest advancements in Generative AI, LLMOps, and ML infrastructure.
Requirements
Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field.
4+ years of experience in machine learning, NLP, or AI development.
Proficiency in Python and libraries like PyTorch, TensorFlow, Hugging Face Transformers.
Solid understanding of LLMs, embeddings, vector databases (Pinecone, Qdrant, Weaviate, FAISS).
Experience with RAG pipelines, fine-tuning, or prompt engineering.
Familiarity with cloud platforms (AWS, GCP, Azure) for ML deployment.
Strong knowledge of APIs, microservices, and containerization (Docker, Kubernetes).
Experience with LangChain / LangGraph LlamaIndex for AI application orchestration.
Knowledge of MLOps / LLMOps pipelines.
Exposure to multimodal AI (text, image, speech).
Hands-on experience with vector search optimizations.
Contributions to open-source AI projects.