Generative Ai – Tech Lead
2 months ago
Technical Leadership:- Lead, mentor, and inspire a team of AI researchers and engineers, fostering a culture of innovation and technical excellence.- Define and execute the technical roadmap and strategy for generative AI projects, ensuringalignment with organizational goals.- Architect, develop, and optimize large-scale generative AI models, focusing on foundational models and their practical applications in text and speech domains.- 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.- 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.- Refine AI infrastructure to support the deployment of optimized models, with a focus on lowlatency, high throughput, and cost efficiency, especially for large-scale models with billions ofparameters.- Implement and experiment with advanced optimization techniques such as quantization, distillation, sparsity, and model compression to enhance performance and scalability.-
Model Development & Application:- Oversee the end-to-end development of text and speech foundational models and their deployment into scalable AI applications and solutions.- Tackle complex technical challenges related to large-scale model development, includingoptimization for performance, latency, and resource efficiency.- 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.- Lead innovation in advanced NLP and speech processing techniques, including transformers, fine tuning of language models, and the development of next-generation generative models.- Develop and integrate model management systems, including version control, reproducibility, and lineage tracking, ensuring robust and reliable AI model deployment.- Focus on real-time monitoring, model drift detection, and continuous performance enhancement post-deployment, leveraging advanced MLOps practices.-
Infrastructure & Deployment:- Optimize AI infrastructure for large-scale training and inference, leveraging GPU clusters, cloudplatforms, custom hardware configurations, and advanced orchestration frameworks like SLURMand Kubernetes.- 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:- Stay at the forefront of AI research in the text and speech domains, applying new techniques tosolve complex technical challenges specific to Indian languages and dialects.- 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 theefficiency and effectiveness of large-scale AI models.- 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:- Collaborate closely with other technical teams to ensure seamless integration of AI models andapplications into the broader technology stack, particularly in contexts relevant to India's diverselandscape.- 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.- Foster a collaborative and innovative team environment.-
Project Management:- Oversee the end-to-end lifecycle of AI projects, from ideation to deployment, ensuring that eachproject aligns with strategic objectives and delivers measurable outcomes.- Coordinate with product managers, data scientists, and engineers to ensure the timely andsuccessful delivery of AI solutions.- Monitor project progress, identifying and mitigating risks, and adjust plans as necessary to meetobjectives 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.-
Demonstrated success in leading AI projects and teams, with a focus on delivering innovative solutions in dynamic environments.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.Preferred Qualifications:-
Experience with developing and deploying speech/text-based large-scale deep models in production environments.-
Familiarity with the latest advancements in NLP and speech processing research, particularly in the context of Indian languages and dialects.-
A strong publication record in top-tier AI conferences or journals is a plus.
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