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Generative AI – Tech Lead

4 weeks ago

Delhi, Delhi, India TIH-IoT Full time
About Us

BharatGen, a government-funded project under the TIH-IoT initiative, is at the forefront of Generative AI innovation. Our mission is to develop a suite of generative AI technology and solutions that capture and reflect the rich linguistic, cultural, socio-economic, and industry-specific diversity of India, driving meaningful impact across millions of lives and various sectors nationwide.

Job Summary

We are seeking an accomplished and technically adept Generative AI Tech Lead with deep expertise in the development, optimization, and deployment of large-scale generative AI models, particularly in text and speech domains. This role involves building foundational models, and creating advanced applications and solutions that address the complex technical challenges of large-scale model development.

Key Responsibilities
  1. Technical Leadership: Lead, mentor, and inspire a team of AI researchers and engineers, fostering a culture of innovation and technical excellence.
  2. Define and Execute Technical Roadmap: Define and execute the technical roadmap and strategy for generative AI projects, ensuring alignment with organizational goals.
  3. Architect and Develop Large-Scale Models: Architect, develop, and optimize large-scale generative AI models, focusing on foundational models and their practical applications in text and speech domains.
  4. Lead Design and Implementation: Lead the design and implementation of robust training and inference pipelines, ensuring scalability, efficiency, and adaptability, particularly for multilingual and multi-dialectal text and speech models.
  5. Drive Innovation: Drive innovation by experimenting with state-of-the-art techniques in NLP, speech recognition, and generative language models, developing solutions that address real-world challenges.
  6. Refine AI Infrastructure: Refine AI infrastructure to support the deployment of optimized models, with a focus on low latency, high throughput, and cost efficiency, especially for large-scale models with billions of parameters.
  7. Implement Advanced Optimization Techniques: Implement and experiment with advanced optimization techniques such as quantization, distillation, sparsity, and model compression to enhance performance and scalability.
Model Development & Application
  1. Oversee End-to-End Development: Oversee the end-to-end development of text and speech foundational models and their deployment into scalable AI applications and solutions.
  2. Tackle Complex Technical Challenges: Tackle complex technical challenges related to large-scale model development, including optimization for performance, latency, and resource efficiency.
  3. Utilize Cutting-Edge Tools and Frameworks: Utilize cutting-edge tools and frameworks like PyTorch, TensorFlow, DeepSpeed, and CUDA to build, deploy, and scale AI models and applications capable of handling diverse linguistic data across modalities.
  4. Lead Innovation in Advanced NLP and Speech Processing Techniques: Lead innovation in advanced NLP and speech processing techniques, including transformers, fine tuning of language models, and the development of next-generation generative models.
  5. Develop and Integrate Model Management Systems: Develop and integrate model management systems, including version control, reproducibility, and lineage tracking, ensuring robust and reliable AI model deployment.
  6. Focus on Real-Time Monitoring and Model Drift Detection: Focus on real-time monitoring, model drift detection, and continuous performance enhancement post-deployment, leveraging advanced MLOps practices.
Infrastructure & Deployment
  1. Optimize AI Infrastructure: Optimize AI infrastructure for large-scale training and inference, leveraging GPU clusters, cloud platforms, custom hardware configurations, and advanced orchestration frameworks like SLURM and Kubernetes.
  2. Integrate Advanced MLOps Frameworks: Integrate and tailor advanced MLOps frameworks such as KubeFlow, MosaicML, and Terraform to support robust development, deployment, and monitoring cycles for AI applications.
Research & Innovation
  1. Stay at the Forefront of AI Research: Stay at the forefront of AI research in the text and speech domains, applying new techniques to solve complex technical challenges specific to Indian languages and dialects.
  2. Lead Research Initiatives: Lead research initiatives focused on cutting-edge NLP and speech processing innovations, with an emphasis on techniques like sparsity, model pruning, and knowledge distillation to enhance the efficiency and effectiveness of large-scale AI models.
  3. Foster a Culture of Continuous Experimentation: Foster a culture of continuous experimentation, encouraging the use of emerging AI techniques and frameworks to push the boundaries of what's possible in generative AI, particularly in the Indian context.
Collaboration
  1. Collaborate with Technical Teams: Collaborate closely with other technical teams to ensure seamless integration of AI models and applications into the broader technology stack, particularly in contexts relevant to India's diverse landscape.
  2. Work with Product Teams: Work with product teams to translate national and regional needs into technical solutions, ensuring that the AI models and applications developed have practical, real-world impact.
  3. Foster a Collaborative and Innovative Team Environment: Foster a collaborative and innovative team environment.
Project Management
  1. Oversee End-to-End Lifecycle: Oversee the end-to-end lifecycle of AI projects, from ideation to deployment, ensuring that each project aligns with strategic objectives and delivers measurable outcomes.
  2. Coordinate with Product Managers and Engineers: Coordinate with product managers, data scientists, and engineers to ensure the timely and successful delivery of AI solutions.
  3. Monitor Project Progress: Monitor project progress, identifying and mitigating risks, and adjust plans as necessary to meet objectives and deadlines.

Education: Ph.D. in Computer Science, AI/ML, or a related field with 5+ years of relevant experience, or an MS with 8+ years of experience in applied AI/ML.

Experience: Extensive hands-on experience in developing and deploying large-scale generative AI models and their applications, particularly in the text and speech domains. Proven expertise in high-performance computing, with proficiency in Python, C/C++, CUDA, and the optimization of NLP and speech models. Strong track record in optimizing AI models for training, inference, and real-world applications, with practical knowledge of tools like DeepSpeed, FSDP, and advanced MLOps frameworks.

Skills: Deep understanding of generative AI techniques specific to NLP and speech processing, including transformers and advanced language models. Strong programming skills in Python, C/C++, with deep experience in GPU acceleration, CUDA, and kernel level programming for AI applications. Experience with advanced optimization and acceleration techniques, including quantization, distillation, and model compression. Proficiency with cutting-edge AI frameworks and tools, including DeepSpeed, FSDP, and advanced MLOps platforms like KubeFlow and Terraform. Familiarity with large-scale model management, including version control, model lineage tracking, and real-time monitoring in production environments. Exceptional problem-solving abilities with a focus on innovation, efficiency, and the development of scalable AI solutions. Strong communication and interpersonal skills, with the ability to articulate complex technical concepts to diverse audiences. Ability to thrive in a fast-paced, dynamic environment, managing multiple priorities and driving results.