AI Engineer

Responsibilities:

• Ideate, Design, Develop, and Implement: Create end-to-end Generative AI, Computer Vision and Audio Processing pipelines and models, including data processing, model fine-tuning, evaluation, and deployment for real-estate & fintech applications.
• LLM Application Development: Architect and build sophisticated AI solutions using frameworks like BAML, LlamaIndex, and the Hugging Face ecosystem. Apply knowledge of advanced techniques like Retrieval-Augmented Generation (RAG) and agentic systems.
• Vector Embeddings & Databases: Implement multimodal vector embedding strategies and manage vector databases for efficient similarity search and information retrieval.
• Diffusion Model Expertise: Implement, test, fine-tune (using techniques like LoRA), and deploy diffusion models, with specific experience in Stable Diffusion, Flux and WAN 2.1. Develop applications leveraging these models.
• Model & Platform Ownership: Take ownership of leveraging and managing AI platforms and APIs such as Gemini API, OpenAI, Anthropic, Groq, and open-source models.
• Cross-Functional Collaboration: Work closely with product managers and software engineers to integrate AI capabilities into user-facing products and internal systems.
• Business Alignment: Collaborate effectively with Business teams to understand requirements, translate needs into technical specifications, and demonstrate the value of AI solutions.
• Continuous Learning & Innovation: Stay abreast of the rapidly evolving AI landscape, evaluate emerging models, tools, and techniques, and proactively apply them to drive innovation.
• Documentation & Best Practices: Maintain comprehensive documentation for AI models, pipelines, experiments, and deployment processes. Adhere to MLOps best practices.