Hiring For Sr. Gen AI Engineer Role

Job Description

Senior Generative AI Engineer We are seeking a highly skilled and motivated Senior Generative AI Engineer to join our growing AI team. In this role, you will be instrumental in designing, developing, fine-tuning, and deploying cutting-edge generative AI models and applications. You will work on challenging projects, leveraging state-of-the-art techniques like Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multimodal approaches to solve real-world problems. If you have a passion for building intelligent systems and staying at the forefront of AI advancements, we want to hear from you. Responsibilities : • Design, implement, and optimize sophisticated generative AI models (LLMs, diffusion models, transformers, etc.) for various applications. • Develop and refine techniques for fine-tuning pre-trained models on domain-specific datasets to enhance performance and relevance. • Implement and iterate on advanced Retrieval-Augmented Generation (RAG) systems, including selecting appropriate vector databases, optimizing embedding strategies, and refining retrieval/generation pipelines. • Engineer effective prompts and interaction strategies for maximizing the capabilities of generative models. • Process and prepare large datasets for training and fine-tuning generative models. • Evaluate model performance rigorously using appropriate metrics and benchmarks, focusing on accuracy, relevance, safety, and efficiency. • Collaborate with data scientists, software engineers, MLOps engineers, and product managers to integrate generative AI capabilities into products and services. • Optimize models and algorithms for efficient training and inference on cloud platforms (AWS, GCP, Azure). Job Opening • Contribute to the team's MLOps practices by versioning models/data, containerizing applications (e.g., Docker), and deploying models via established CI/CD pipelines. • Stay current with the latest research papers, open-source projects, and industry trends in generative AI, RAG, and related fields. • Troubleshoot and debug complex issues in AI models and systems. • Potentially mentor junior engineers and contribute to internal knowledge sharing. Qualifications : • Typically, 5+ years of professional experience in software engineering or machine learning, with at least 2-3 years of focused, hands-on experience building and implementing Generative AI models and systems. • Strong understanding of the fundamentals of deep learning, natural language processing (NLP), and machine learning principles. • Proven experience working with major generative AI model architectures (e.g., Transformers, GANs, VAEs, Diffusion Models) and foundational models (e.g., GPT series, Llama series, Stable Diffusion). • Hands-on experience with fine-tuning large pre-trained models. • Practical experience implementing RAG pipelines, including familiarity with vector databases (e.g., Pinecone, Weaviate, Milvus, ChromaDB, FAISS) and embedding techniques. • Expert-level programming skills in Python. • Proficiency with major AI/ML frameworks such as PyTorch or TensorFlow/Keras, and libraries within the ecosystem (e.g., Hugging Face Transformers, LangChain, LlamaIndex). • Experience using cloud platforms (AWS, GCP, or Azure) for developing, training, and deploying machine learning models (e.g., using services like SageMaker, Vertex AI, Azure ML). • Strong analytical and problem-solving skills. • Excellent communication and collaboration skills. Preferred Qualifications : • • Degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. • • Experience with multimodal generative models (text-to-image, image-to-text, etc.). • • Familiarity with Reinforcement Learning from Human Feedback (RLHF) concepts or implementation. • • Experience with MLOps tools and platforms (e.g., MLflow, Kubeflow, DVC). • • Experience with distributed training frameworks (e.g., DeepSpeed, PyTorch Distributed). • • Experience deploying models into production using containerization (Docker, Kubernetes) and API frameworks (e.g., FastAPI, Flask). • • Contributions to open-source AI/ML projects or publications in relevant conferences/journals