Key skills
Generative AILarge language modelsPythonML frameworksNLPDeep learningDockerKubernetesPrompt engineeringFine-tuning strategiesProduction model monitoringAIArtificial IntelligenceMachine LearningMLDeep LearningLLMLarge Language ModelsPyTorchTensorFlowLangChainQdrantPineconeWeaviate
About this role
Keystone Recruitment is seeking a highly skilled and motivated Generative AI Engineer to design, develop, and deploy advanced AI solutions leveraging large language models and generative architectures. This senior-level role focuses on building production-grade AI systems across various use cases including content generation and conversational AI.
Responsibilities:
- Develop and fine-tune large language models (such as GPT-class, LLaMA-family, Mistral-family, Claude-class models) for domain-specific downstream tasks
- Design and optimize Retrieval-Augmented Generation (RAG) pipelines using frameworks like LangChain, LlamaIndex, or Haystack
- Build end-to-end generative AI applications spanning text, code, image, and audio use cases
- Implement embedding-based retrieval systems using vector databases such as FAISS, Pinecone, Weaviate, or Qdrant
- Integrate foundation model APIs into scalable production workflows
- Work with multimodal models and generative systems for image, vision-language, and audio tasks
- Optimize model latency, throughput, and scalability for production environments
- Collaborate cross-functionally with ML engineers, data teams, and product stakeholders
- Stay current with advancements in generative AI research and apply them to real-world systems
Requirements:
- Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
- Strong hands-on experience in ML/AI engineering, with demonstrated experience in generative AI or LLM-based systems
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Experience using HuggingFace Transformers, LangChain, or similar ecosystems
- Strong understanding of NLP, transformer architectures, embeddings, and deep learning fundamentals
- Experience building scalable ML pipelines and deploying models using Docker, Kubernetes, or similar infrastructure tools
- Familiarity with prompt engineering, fine-tuning strategies, evaluation methodologies, and production model monitoring