GEN AI Engineer

Job Title: Generative AI (GenAI) Engineer

Location: [City, State or Remote]

Job Type: [Full-Time / Part-Time / Contract]

Department: AI/ML Engineering

Reports To: Head of AI / CTO / Engineering Manager

Job Summary:

We are seeking a skilled and creative Generative AI Engineer to join our team to design, build, and optimize generative models that drive innovation and next-generation AI products. You will work closely with data scientists, machine learning engineers, and product teams to deploy state-of-the-art AI systems across a wide range of applications, including text, image, audio, and code generation.

Key Responsibilities:

• Design, develop, and fine-tune generative AI models (e.g., GPT, DALL E, Stable Diffusion, LLaMA, etc.).

• Train and deploy large language models (LLMs) and multimodal models for production use cases.

• Work with datasets for supervised, unsupervised, or reinforcement learning approaches.

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

• Develop scalable APIs and pipelines for serving generative models.

• Apply techniques such as transfer learning, prompt engineering, fine-tuning, and model distillation.

• Monitor and evaluate model performance, bias, fairness, and safety.

• Research and experiment with cutting-edge advancements in the generative AI domain.

• Optimize models for inference speed, cost efficiency, and user experience.

Required Qualifications:

• Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (PhD is a plus).

• 2+ years of experience in ML/AI engineering, with hands-on work in generative models.

• Strong programming skills in Python; experience with ML frameworks like PyTorch, TensorFlow, Hugging Face Transformers, LangChain, etc.

• Solid understanding of transformer architectures and LLMs.

• Experience in training and fine-tuning foundation models using GPUs and distributed systems.

• Familiarity with data pipelines, cloud services (AWS, Google Cloud Platform, or Azure), and MLOps practices.

• Understanding of prompt engineering, RLHF (Reinforcement Learning with Human Feedback), and embeddings.

Preferred Qualifications:

• Experience with retrieval-augmented generation (RAG), vector databases (e.g., FAISS, Pinecone).

• Familiarity with open-source GenAI tools and libraries (e.g., LLaMA, Mistral, OpenAI, Anthropic APIs).

• Knowledge of multi-modal generative models (e.g., image-to-text, text-to-video).

• Experience deploying models using containers (Docker), Kubernetes, and CI/CD workflows.

• Contributions to research papers, open-source projects, or GenAI communities.