Senior Python GEN AI Developer

• Develop and deploy GenAI-based applications to solve complex business problems.
• Implement RAG frameworks to enhance information retrieval and response accuracy.
• Design and optimize document chunking strategies tailored to specific data types and use cases.
• Build and manage data embeddings using various vector databases for high-performance similarity searches.
• Collaborate with data engineers and scientists to integrate AI solutions seamlessly into existing pipelines.
• Explore and implement best practices for leveraging Amazon Bedrock and Azure OpenAI services.
• Stay updated on emerging trends and technologies in the GenAI and AI/ML landscape.

What we're looking for

• Proficiency in Generative AI frameworks and technologies like
  • Prompt Engineering – creating specific prompts to guide LLM outputs
  • Fine Tuning and Few-Shot learning – Adapting LLMs for domain-specific tasks.
  • Retrieval-Augmented Generation (RAG) – Combining LLMs with external data retrieval systems
  • Text-to-Text Transfer Transformation (T5) – Framework for translating tasks into text forms.
• Expertise in designing and implementing chunking strategies for diverse datasets.
• Strong knowledge of data embedding techniques and working experience with vector databases like Pinecone, Weaviate, or Milvus.
• Solid programming skills in Python, with experience in AI/ML libraries such as TensorFlow, PyTorch, or Hugging Face Transformers.
• Familiarity with cloud platforms and services for AI/ML workloads (AWS or Azure).
• Experience with API integration for AI services and building scalable applications.

What you should expect in this role

• Develop and deploy GenAI-based applications to solve complex business problems.
• Implement RAG frameworks to enhance information retrieval and response accuracy.
• Design and optimize document chunking strategies tailored to specific data types and use cases.
• Build and manage data embeddings using various vector databases for high-performance similarity searches.
• Collaborate with data engineers and scientists to integrate AI solutions seamlessly into existing pipelines.
• Explore and implement best practices for leveraging Amazon Bedrock and Azure OpenAI services.
• Stay updated on emerging trends and technologies in the GenAI and AI/ML landscape.