Sr Developer

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What We'll Bring:

What You'll Bring:

Years of Experience: 5-10 Years

Key Responsibilities

• Design, develop, and optimize generative AI models with a strong focus on RAG and Graph RAG.
• Implement state-of-the-art techniques for retrieval-augmented generation and graph-based AI models.
• Apply RAG models to enhance information retrieval and generation tasks, improving the quality and accuracy of AI outputs.
• Develop custom AI models and fine-tune pre-trained models for specific client use cases
• Optimize generative models for production, balancing performance, latency
• Design and implement efficient data pipelines for training and serving generative models
• Develop strategies for effective prompt engineering and few-shot learning in production systems
• Implement robust evaluation frameworks for generative AI outputs
• Address challenges related to bias, fairness, and ethical considerations in generative AI applications

• Develop internal tools and frameworks to accelerate generative AI development
• Mentor and guide junior developers in generative AI techniques and best practices.
• Stay updated with the latest research in generative AI, RAG, and Graph RAG, and apply new methodologies to improve model outcomes.
•  Experiment with different model architectures and approaches to achieve optimal results.

Project Delivery

• Lead the technical aspects of generative AI projects from pilot to production
• Deploy AI models into production environments, ensuring scalability, efficiency, and reliability.
• Collaborate with DevOps teams to seamlessly integrate models into existing systems and workflows.
• Develop proof-of-concepts and prototypes to demonstrate the potential of generative AI in solving client problems
• Conduct technical feasibility studies for applying generative AI to novel use cases
• Implement monitoring and observability solutions for deployed generative models
• Troubleshoot and optimize generative AI systems in production environments

Technical Skills

• Required
  • Strong programming skills in languages such as Python, Java, or C++
  • Solid understanding of machine learning algorithms and techniques
  • Demonstrated experience with latest large language models (LLMs)
  • Practical understanding of generative AI frameworks (e.g., Hugging Face Transformers, OpenAI GPT, LlamaIndex, Jarvis etc)
  • Extensive experience with RAG (Retrieval-Augmented Generation) and GraphRAG models.
  • Familiarity with prompt engineering and few-shot learning techniques
  • Expertise in MLOps and LLMOps practices, including CI/CD for ML models
  • Strong knowledge of one or more cloud-based AI services (e.g., AWS Bedrock, Azure ML, Google Vertex AI)
  • Deep understanding of state-of-the-art AI architectures (e.g., Transformers, VAEs, GANs, Diffusion Models)
  • Proficiency in Python and software engineering best practices for AI systems

• Preferred
  • Proficiency in optimizing generative models for inference (quantization, pruning, distillation)
  • Experience with distributed training of large-scale AI models
  • Advanced Techniques: Experience with reinforcement learning, unsupervised learning, or graph-based learning models.
  •  Agile Methodologies: Experience working in Agile/Scrum environments.
  •  Version Control: Proficiency with Git and other version control systems.

Impact You'll Make:

TBD

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