Generative AI Engineer Manager (Hyderabad)

Manager (AI Hub - GDC) Roles & responsibilities Here are some of the key responsibilities of Sr AI Research Scientist: 1.Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). 2.Experience with POCs on emerging and latest innovation in AI. 3.Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. 4.Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. 5.Design and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. 6.Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. 7.Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. 8.Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. 9.Technical Leadership: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. Ability to drive multiple teams and cross-collaborate to ensure the quality delivery. 10.Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. 11.Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. Mandatory technical & functional skills Machine learning frameworks - PyTorch or TensorFlow. Deep Learning algorithms - CNN, RNN, LSTM, Transformers LLMs ( BERT, GPT, etc.) and NLP algorithms. Fine Tuning of Open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc. Stay current with AI trends - MCP, A2A protocol, ACP, etc. GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker Scientific understanding - PEFT - LORA, QLORA, etc. Exposure to GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker Experience with proof of concepts on emerging and latest innovation in AI Preferred technical & functional skills Langgraph/CrewAI/Autogen Large scale deployment of GenAI/DL/ML projects, with good understanding of MLOps /LLM Ops Ensure scalability and efficiency, handle data tasks, Cloud computing experience- Azure/AWS/GCP BigQuery/Synapse Key behavioral attributes/requirements Ability to mentor junior developers Ability to own project deliverables, not just individual tasks Understand business objectives and functions to support data needs Roles & responsibilities Here are some of the key responsibilities of Sr AI Research Scientist: 1. Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a solid publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). 2. Experience with POCs on emerging and latest innovation in AI. 3. Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. 4. Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. 5. Design and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the Mana