GenAI Engineer

We are looking for a Generative AI Engineer with 2–4 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.
• Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field.
• 2–4 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).

Required Skills & Qualifications

Preferred Skills (Nice To Have)

• Experience with LangChain / 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.