Data Science Manager
6 days ago
Description : Job brief : An ideal candidate will have 7 to 12 years of experience working in live projects related to Artificial : - Lead the design and implementation of enterprise-grade Machine Learning, Deep Learning and Generative AI solutions.- Define and execute the AI technology projects, aligning Generative AI, Machine Learning, and Deep Learning initiatives with business strategy and data architecture.- Design, evaluate, and guide development of autonomous and agentic AI systems capable of reasoning, planning, and tool-based decision-making.- Architect end-to-end RAG pipelines including document processing, embedding generation, vector storage, retrieval mechanisms, and contextual response systems.- Oversee Machine Learning and Deep Learning model development, training, deployment, and optimization for predictive and generative use cases.- Establish robust LLMOps / MLOps pipelines ensuring model lifecycle management, observability, version control, and performance monitoring.- Collaborate with data engineering, analytics, and DevOps teams to integrate AI systems with enterprise data warehouse/lakes, APIs, and reporting environments.- work with stakeholders to identify, evaluate, and prioritize AI-driven business opportunities, focusing on measurable impact and innovation.- Provide technical leadership, mentorship, and guidance to AI engineers, data scientists, and developers, fostering a high-performance innovation culture.- Stay ahead of emerging AI research, frameworks, and tools, driving continuous evolution of enterprise AI capabilities.Skills Areas : - Deep expertise in Generative AI, Large Language Models (GPT, Claude, Llama, Mistral, etc.), and RAG architectures.- Strong proficiency in Agentic AI concepts - autonomous agents, multi-agent orchestration, and reasoning loops.- Advanced understanding of Machine Learning (ML) and Deep Learning (DL) algorithms, architectures (Transformers, CNNs, RNNs), and model deployment workflows.- Experience architecting solutions with AWS agentic AI framework, LangChain, LlamaIndex, Semantic Kernel, or similar frameworks.- Hands-on experience with RDBMS, No SQL and vector databases.- Proficiency in Python, scikit-learn, PyTorch, TensorFlow, and Hugging Face Transformers.- Expertise in LLMOps / MLOps practices model training, CI/CD, observability, and drift management.- Deep understanding of cloud-native AI architecture (AWS Sagemaker, Azure AI, GCP Vertex AI).- Strong foundation in AI governance, ethical AI, and compliance frameworks.- Excellent leadership, communication, and stakeholder management skills to align AI initiatives with business goals.Good-to-Have Skills : - Experience in Data Engineering ETL/ELT pipeline design, data modeling, data warehousing, and integration with AI pipelines.- Familiarity with Data Visualization and BI tools such as Power BI, Tableau, Amazon QuickSight especially for building analytical insights from AI-driven data.- Understanding of DataOps and analytics architectures supporting AI and decision intelligence systems.- Knowledge of enterprise data platforms and API-based data integration to connect AI models with structured and unstructured data sources.Experience : - The ideal candidate will have relevant experience of > 7 years.- Possession of a professional degree / post-graduation is desirable.- Certifications in the respective technology areas will be an added advantage. (ref:hirist.tech)
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