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What We'll Bring:
Job Summary
As a Manager for Generative AI team, you will play a pivotal role in defining architecture, designing, developing, evaluating and deploying advanced generative AI models. You will be responsible for leveraging different architectures and RAG models to create sophisticated AI-driven solutions. This role requires a deep understanding of generative models, machine learning algorithms, and the ability to deliver on complex AI projects.
Years of Experience: 14+ Years
Key Responsibilities
&bull Design, develop, and optimize generative AI models with a strong focus on RAG and Graph RAG.
&bull Implement state-of-the-art techniques for retrieval-augmented generation and graph-based AI models.

• 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

• Provide expert technical guidance on generative AI capabilities and limitations to clients
• Collaborate with solution architects to design generative AI-powered solutions that meet client needs
• Present technical approaches and results to both technical and non-technical stakeholders
• Assist in scoping and estimating generative AI projects

• Stay at the forefront of generative AI research and industry trends
• Contribute to the company's intellectual property through patents or research publications
• Develop internal tools and frameworks to accelerate generative AI development
• Mentor junior team members on generative AI technologies and best practices
• Contribute to technical blog posts and whitepapers on generative AI applications

• 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)
  • Expertise in PyTorch or TensorFlow, with a preference for experience in both
  • 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.

• Advanced degree in Computer Science, Machine Learning, or related field with a focus on generative models
• 12+ years of hands-on experience developing and deploying AI models in production environments with 2+ years of experience in developing generative AI pilots, proofs of concept, and prototypes