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 .