Job Title: Generative AI Engineers Location: Onsite Employment Type: Full-Time Experience Level: Senior Role Overview Multiple senior-level opportunities for a Generative AI Engineers to join a forward-leaning technology team focused on building next-generation AI applications using LLMs, multimodal transformers, and advanced generative models. The role involves hands-on development of tools for content generation, autonomous agents, intelligent assistants, and synthetic data systems — with direct impact on real-world products and workflows. This is a high-impact position for someone who wants to push the limits of applied AI and help shape scalable solutions at the frontier of generative intelligence. Key Responsibilities • Fine-tune and deploy LLMs (e.g., GPT, LLaMA, Claude, Mistral) for targeted downstream use cases • Build and optimise RAG pipelines using LangChain, LlamaIndex, Haystack, etc. • Develop full-stack GenAI applications across modalities (text, code, image, audio) • Leverage embedding stores and vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) • Integrate foundation model APIs into production systems • Work with multimodal architectures (e.g., CLIP, DALL·E, Stable Diffusion, Gemini) • Ensure performance, scalability, and latency targets in production environments • Collaborate with data, ML, and product teams to define and implement GenAI features • Stay ahead of developments in generative AI and proactively apply new techniques Required Qualifications • Bachelor's or Master’s in Computer Science, Machine Learning, or related discipline • Strong experience in ML/AI engineering, especially LLM-based systems • Proficient in Python with libraries such as HuggingFace Transformers, LangChain, PyTorch, TensorFlow • Deep understanding of NLP, transformers, and generative model architectures • Track record of deploying models in production (Docker, Kubernetes, etc.) • Experience with prompt engineering, fine-tuning, and model evaluation Preferred Qualifications • Familiarity with LLMOps platforms (e.g., Weights & Biases, MLflow, BentoML) • Understanding of ethical, privacy, and safety challenges in GenAI applications • Experience with LoRA, PEFT, or model quantisation techniques • Exposure to agentic and multi-agent frameworks (e.g., AutoGPT, CrewAI, LangGraph) • Contributions to open-source GenAI projects or frameworks