Gen AI Python Developer

We are seeking a skilled Generative AI Engineer with a strong background in Python to

join our dynamic team. In this role, you will integrate backend development expertise

with the latest advancements in AI to create impactful solutions. If you excel in a

fast-paced environment and enjoy tackling complex challenges, we encourage you to

apply.

Key Responsibilities:

Generative AI Development

• Develop and implement generative AI models using frameworks like LangChain or Llama-Index.
• Apply prompt engineering techniques to design effective queries and ensure optimal LLM responses for diverse use cases.
• Master advanced LLM functionalities, including prompt optimization, hyperparameter tuning, and response caching.
• Implement Retrieval-Augmented Generation (RAG) workflows by integrating vector databases like Pinecone, Weaviate, Supabase, or PGVector for efficient similarity searches.
• Work with embeddings and build solutions that leverage similarity search for personalized query resolution.
• Explore and process multimodal data, including image and video understanding and generation.
• Integrate observability tools for monitoring and evaluating LLM performance to ensure system reliability.
• Backend Engineering, Build and maintain scalable backend systems using Python frameworks such as FastAPI, Django, or Flask.
• Design and implement RESTful APIs for seamless communication between systems and services.
• Optimize database performance with relational databases (PostgreSQL, MySQL) and integrate vector databases (Pinecone, PGVector, Weaviate, Supabase) for advanced AI workflows.
• Implement asynchronous programming and adhere to clean code principles formal maintainable, high-quality code.
• Seamlessly integrate third-party SDKs and APIs, ensuring robust interoperability with external systems.
• Develop backend pipelines for handling multimodal data processing, and supporting text, image, and video workflows.
• Manage and schedule background tasks with tools like Celery, cron jobs, or equivalent job queuing systems.
• Leverage containerization tools such as Docker for efficient and reproducible deployments.
• Ensure security and scalability of backend systems with adherence to industry best practices.